Assess component readiness #10
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…e features Co-authored-by: garveyht <[email protected]>
WalkthroughThis update introduces a production-ready MCP Agent Stack with extensive new documentation, a multi-stage Docker build, Docker Compose orchestration, Prometheus and Grafana monitoring, and a robust, asynchronous agent pipeline. The codebase is modularized with configuration, data models, monitoring, and API layers, accompanied by comprehensive production-level tests. Changes
Sequence Diagram(s)sequenceDiagram
participant Client
participant API (FastAPI)
participant Orchestrator
participant LoadBalancer
participant Agents
participant Monitoring
Client->>API (FastAPI): POST /process (content)
API (FastAPI)->>Orchestrator: run(content, request_id)
Orchestrator->>LoadBalancer: distribute(ProcessingRequest)
LoadBalancer->>Agents: process (DataParserAgent)
Agents-->>LoadBalancer: parsed_data
LoadBalancer->>Agents: process (LoggerAgent)
Agents-->>LoadBalancer: log_entry
LoadBalancer->>Agents: process (SummarizerAgent)
Agents-->>LoadBalancer: summary
LoadBalancer->>Agents: process (OptimizerAgent)
Agents-->>LoadBalancer: optimized_content
LoadBalancer->>Agents: process (LoggerAgent)
Agents-->>LoadBalancer: log_entry
LoadBalancer->>Monitoring: record_request, update_metrics
LoadBalancer-->>Orchestrator: ProcessingResponse
Orchestrator-->>API (FastAPI): ProcessingResponse
API (FastAPI)-->>Client: JSON Response
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Summary of Changes
Hello @groupthinking, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly upgrades the MCP Agent Stack, moving it from a prototype to a production-ready application. The changes focus on improving performance through asynchronous processing, enhancing reliability with comprehensive monitoring and structured logging, and bolstering security with input validation and secure container configurations. It also provides a complete deployment solution using Docker and a user-friendly FastAPI interface, all backed by a robust testing framework and detailed documentation.
Highlights
- Production Readiness: The MCP Agent Stack has been transformed into a production-ready system, incorporating enterprise-grade features for live deployment, enhanced functionality, and robust operations.
- Asynchronous Processing: Introduced full
async/awaitsupport across the agent orchestration, enabling high-performance concurrent request handling and improved scalability. - Comprehensive Monitoring & Observability: Integrated Prometheus for metrics collection (request rates, latency, errors, system usage), structured logging (JSON-formatted with correlation IDs), and health checks for robust system oversight.
- Security Hardening: Implemented critical security measures including comprehensive input validation with configurable limits, robust error handling, and containerization best practices like running non-root containers.
- Containerization & Deployment: Added full Docker and Docker Compose support, including multi-stage Dockerfiles, a
docker-compose.ymlfor easy setup with Prometheus and Grafana, and a detailedDEPLOYMENT.mdguide. - API-First Design: A new FastAPI web server has been integrated, providing a modern REST API with OpenAPI documentation, multiple endpoints (health, metrics, process, config), and CORS support.
- Enhanced Testing Suite: A comprehensive testing suite has been added, covering unit, integration, and production-specific tests, including async testing and error scenario validation.
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Code Review
This pull request is a major step towards making the MCP Agent Stack production-ready. It introduces a wealth of new features, including a FastAPI server, Docker support, comprehensive monitoring, and a testing suite. The overall architecture is much more robust.
I've identified some critical issues that need to be addressed, including a logic bug in the agent pipeline, a security vulnerability with a hardcoded password, and the use of a deprecated Pydantic feature that will cause a crash. I've also provided feedback on security, maintainability, and production best practices.
Once these issues are resolved, the system will be in a much stronger position for a production launch. Great work on this significant refactoring effort!
| ports: | ||
| - "3000:3000" | ||
| environment: | ||
| - GF_SECURITY_ADMIN_PASSWORD=admin |
There was a problem hiding this comment.
Hardcoding default credentials like GF_SECURITY_ADMIN_PASSWORD=admin is a critical security vulnerability. This password will be checked into version control, exposing it to anyone with repository access. In a production environment, this could lead to unauthorized access to your monitoring dashboards.
This credential should be managed as a secret. I recommend using an environment variable that is loaded from a .env file (which is git-ignored) or a secrets management tool.
- GF_SECURITY_ADMIN_PASSWORD=${GF_ADMIN_PASSWORD}
| async def distribute(self, request: ProcessingRequest) -> ProcessingResponse: | ||
| """Process request through the agent pipeline with full monitoring.""" | ||
| start_time = time.time() | ||
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| try: | ||
| # Step 1: Parse data | ||
| self.logger.info("Starting data parsing", request_id=request.request_id) | ||
| parsed_data = await self.agents[0].process(request.content) | ||
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| # Step 2: Log parsing completion | ||
| await self.agents[1].process(f"Data parsed successfully for request {request.request_id}") | ||
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| # Step 3: Summarize content | ||
| self.logger.info("Starting summarization", request_id=request.request_id) | ||
| summary = await self.agents[2].process(parsed_data) | ||
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| # Step 4: Log summarization completion | ||
| await self.agents[3].process(f"Content summarized for request {request.request_id}") | ||
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| # Step 5: Optimize content | ||
| self.logger.info("Starting optimization", request_id=request.request_id) | ||
| optimized_content = await self.agents[4].process(summary) | ||
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| # Step 6: Final logging | ||
| await self.agents[5].process(f"Processing completed for request {request.request_id}") | ||
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| processing_time = (time.time() - start_time) * 1000 # Convert to milliseconds | ||
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| return ProcessingResponse( | ||
| request_id=request.request_id, | ||
| processed_content=optimized_content, | ||
| summary=summary, | ||
| processing_time_ms=processing_time, | ||
| agent_metadata={ | ||
| "parsed_data": parsed_data, | ||
| "agents_used": [agent.name for agent in self.agents] | ||
| } | ||
| ) | ||
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| except Exception as e: | ||
| processing_time = (time.time() - start_time) * 1000 | ||
| self.logger.error("Processing failed", | ||
| request_id=request.request_id, | ||
| error=str(e), | ||
| processing_time_ms=processing_time) | ||
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| return ProcessingResponse( | ||
| request_id=request.request_id, | ||
| processed_content="", | ||
| summary="", | ||
| processing_time_ms=processing_time, | ||
| success=False, | ||
| error_message=str(e) | ||
| ) |
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There is a critical logic bug in the distribute method that breaks the agent pipeline. The sequence of agent calls is incorrect, and it attempts to access an agent at an index that is out of bounds.
Here's a breakdown of the issues:
- The
AgentFactorycreates a list of 5 agents:[DataParser, Summarizer, Logger, Optimizer, Logger]. - The
distributemethod expects a different order and makes 6 agent calls, leading to anIndexErroronself.agents[5]. - There are type mismatches. For example,
self.agents[1](theSummarizerAgent) is called with a string, but it expects a dictionary.self.agents[2](theLoggerAgent) is called with a dictionary but expects a string.
This will cause the pipeline to fail or produce incorrect results. The agent list in the factory and the processing logic in the load balancer must be synchronized. The agents_minimal.py file seems to have the correct logic, which could be a good reference.
| @validator('content') | ||
| def validate_content(cls, v): | ||
| if len(v) > 10000: # 10KB limit | ||
| raise ValueError("Content too large. Maximum 10,000 characters allowed.") | ||
| return v.strip() |
There was a problem hiding this comment.
This validator has several issues:
- Critical Correctness Issue: The
@validatordecorator is deprecated in Pydantic v2 (which is specified inrequirements.txt) and will raise an error at runtime. You should use@field_validatorfrompydanticand decorate the method with@classmethod. - Redundant Logic: The input size is already being validated in
DataParserAgent. Having validation in multiple places can lead to inconsistencies.
To fix this and improve the design, I recommend removing this validator from the model entirely and centralizing the validation at the API entry point (src/api.py). This keeps the data model clean and ensures validation happens once, using the configurable max_input_size from settings.
| services: | ||
| mcp-agent-stack: | ||
| build: . | ||
| container_name: mcp-agent-stack |
There was a problem hiding this comment.
Using container_name is convenient for development but prevents horizontal scaling. If you try to run docker-compose up --scale mcp-agent-stack=2, it will fail because container names must be unique.
For a production-ready setup that supports scaling, it's better to remove the container_name directives and let Docker Compose generate unique names for each container instance.
# container_name: mcp-agent-stack
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| # Optional: Prometheus for metrics collection | ||
| prometheus: | ||
| image: prom/prometheus:latest |
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Using the latest tag for Docker images is not recommended for production environments. It can lead to unexpected behavior or breaking changes when the image is updated, making deployments unpredictable and difficult to reproduce.
Please pin the image to a specific version (e.g., prom/prometheus:v2.47.2) to ensure consistent and stable deployments.
image: prom/prometheus:v2.47.2
| # Add CORS middleware | ||
| app.add_middleware( | ||
| CORSMiddleware, | ||
| allow_origins=["*"], # Configure appropriately for production |
There was a problem hiding this comment.
Allowing all origins with allow_origins=["*"] is a security risk in a production environment. It makes your API vulnerable to Cross-Site Request Forgery (CSRF) attacks from any website.
For production, you should restrict this to a specific list of trusted domains that are allowed to access the API.
| allow_origins=["*"], # Configure appropriately for production | |
| allow_origins=["https://your-frontend-domain.com"], # Configure appropriately for production |
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| ## License | ||
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| [Add your license here] |
There was a problem hiding this comment.
The repository is missing a license file. For an open-source project, and especially one intended for production use, it's crucial to have a license to define how others can use, modify, and distribute the code. Without a license, the default copyright laws apply, which can be very restrictive and create legal ambiguity for users and contributors.
Please add a LICENSE file to the root of the repository. You can choose a standard license from choosealicense.com.
| pydantic>=2.0.0 | ||
| python-dotenv>=1.0.0 | ||
| structlog>=23.0.0 | ||
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| # Web framework | ||
| fastapi>=0.104.0 | ||
| uvicorn[standard]>=0.24.0 | ||
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| # Async and performance | ||
| asyncio-mqtt>=0.16.0 | ||
| aiohttp>=3.8.0 | ||
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| # Monitoring and observability | ||
| prometheus-client>=0.17.0 | ||
| psutil>=5.9.0 | ||
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| # Configuration and validation | ||
| pydantic-settings>=2.0.0 | ||
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| # Testing and development | ||
| pytest>=7.0.0 | ||
| pytest-asyncio>=0.21.0 | ||
| pytest-cov>=4.0.0 | ||
| httpx>=0.25.0 | ||
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| # Security | ||
| cryptography>=41.0.0 |
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Using floating dependencies (e.g., pydantic>=2.0.0) can lead to non-reproducible builds, as pip install might pull in newer, potentially breaking versions of packages in the future. For a production-ready system, it's crucial to have deterministic builds.
I strongly recommend pinning the exact versions of all dependencies (e.g., pydantic==2.5.2). You can generate a pinned requirements.txt file by running pip freeze > requirements.txt in your virtual environment.
| content: str = Field(..., description="Content to process", min_length=1) | ||
| request_id: Optional[str] = Field(default_factory=lambda: str(uuid.uuid4())) | ||
| metadata: Optional[Dict[str, Any]] = Field(default_factory=dict) | ||
| priority: int = Field(default=1, ge=1, le=10, description="Processing priority") |
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The priority field is defined in the ProcessingRequest model but it is not used anywhere in the application logic (e.g., in the orchestrator or load balancer to prioritize requests). This is dead code and could be misleading for developers using the API.
If request prioritization is a planned feature, I recommend creating a ticket to track its implementation. Otherwise, it should be removed to keep the API clean.
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| def _setup_metrics(self): | ||
| """Initialize agent-specific metrics.""" | ||
| agents = ['data_parser', 'summarizer', 'optimizer', 'logger'] |
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The list of agent names is hardcoded here. This is brittle because if an agent is added or removed in src/agents.py, this list must be updated manually. Forgetting to do so would result in missing metrics for the new agents.
To make this more maintainable, you could pass the list of agents from the Orchestrator to the MetricsCollector during initialization, or have the AgentFactory provide the list of agent names.
| agents = ['data_parser', 'summarizer', 'optimizer', 'logger'] | |
| # This should be dynamically populated based on the agents from AgentFactory | |
| # to avoid manual synchronization. | |
| agents = ['data_parser', 'summarizer', 'optimizer', 'logger'] |
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Actionable comments posted: 15
🔭 Outside diff range comments (1)
requirements.txt (1)
2-29: Pin dependency versions or add a lock file for reproducible buildsI didn’t find any lock file (e.g.
requirements.lock,Pipfile.lock,poetry.lock, orpyproject.toml) in the repo, andrequirements.txtcurrently only uses loose lower-bounds. This can lead to silent breakage when upstream ships new minor or major releases.Please update
requirements.txtto pin at least the major/minor versions (using~=) or, ideally, exact versions (==), and introduce a lock file that captures the full dependency tree. For example:- fastapi>=0.104.0 + fastapi~=0.104 # locks to 0.104.xApply the same for all entries in
requirements.txt, or run a tool likepip-compile/Dependabot/Renovate to generate and maintain arequirements.lock.• File needing attention:
requirements.txt
• Next steps:
- Narrow version specifiers (
~=) or pin exact versions (==)- Add and commit a lock file (e.g.
requirements.lock), and adjust CI to install from it
🧹 Nitpick comments (15)
src/config.py (1)
38-42: Re-evaluatecase_sensitive = FalseDisabling case sensitivity allows multiple env-var spellings (
APP_NAMEvsapp_name) but also hides typos and clashes. Production configs usually keepcase_sensitive = Trueand enforce a single canonical spelling.-class Config: - env_file = ".env" - env_file_encoding = "utf-8" - case_sensitive = False +class Config: + env_file = ".env" + env_file_encoding = "utf-8" + case_sensitive = Truemonitoring/prometheus.yml (1)
19-19: Add trailing newline to satisfy linters & POSIX toolsYAML-lint flags the missing final newline. Append a blank line at EOF to avoid diffs in future concatenations and keep tools happy.
docker-compose.yml (2)
10-20: Quote environment values that contain spacesPlain scalars with spaces are valid YAML but error-prone in copy/paste scenarios. Quote or switch to mapping style:
- - APP_NAME=MCP Agent Stack + - APP_NAME="MCP Agent Stack"
67-72: Add final newlineSame lint gripe as with
prometheus.yml; append\nafter the last line.DEPLOYMENT.md (1)
129-137: Convert bare URLs to Markdown linksMarkdownlint warns about bare URLs. Wrap them for readability:
-Access Prometheus at: -- URL: http://localhost:9090 +Access Prometheus at: +- URL: <http://localhost:9090>Dockerfile (1)
63-63: Consider adding workers for production scalability.For production deployments, consider adding the
--workersflag to run multiple worker processes for better performance under load.-CMD ["python", "-m", "uvicorn", "src.api:app", "--host", "0.0.0.0", "--port", "8000"] +CMD ["python", "-m", "uvicorn", "src.api:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"]Note: The number of workers should be configurable via environment variable for different deployment scenarios.
PRODUCTION_READY_SUMMARY.md (1)
1-1: Consider hyphenating the compound adjective.For consistency with the compound adjective usage throughout the document.
-# MCP Agent Stack - Production Ready Summary +# MCP Agent Stack - Production-Ready Summarysrc/models.py (1)
17-21: Clarify the content size limit comment.The comment says "10KB limit" but the validation checks character count, not bytes. This could be misleading since characters can be multi-byte in UTF-8.
@validator('content') def validate_content(cls, v): - if len(v) > 10000: # 10KB limit + if len(v) > 10000: # 10,000 character limit raise ValueError("Content too large. Maximum 10,000 characters allowed.") return v.strip()README.md (2)
24-25: Replace placeholder with actual repository URLThe git clone command contains a placeholder
<repository-url>that should be replaced with the actual repository URL.-git clone <repository-url> +git clone https://github.com/groupthinking/AGENT-STACK.git
195-195: Add license informationThe license section contains a placeholder that should be filled with actual license information.
-[Add your license here] +MIT License (or specify your chosen license)src/api.py (1)
10-13: Use absolute imports for better maintainabilityThe code uses relative imports which can cause issues when the module structure changes. Consider using absolute imports.
-from config import settings -from models import ProcessingRequest, ProcessingResponse, HealthCheck -from agents import Orchestrator -from monitoring import metrics_collector, logger +from src.config import settings +from src.models import ProcessingRequest, ProcessingResponse, HealthCheck +from src.agents import Orchestrator +from src.monitoring import metrics_collector, loggersrc/agents.py (2)
9-11: Use absolute imports for consistencySimilar to api.py, use absolute imports for better maintainability.
-from config import settings -from models import ProcessingRequest, ProcessingResponse -from monitoring import monitoring_middleware, logger +from src.config import settings +from src.models import ProcessingRequest, ProcessingResponse +from src.monitoring import monitoring_middleware, logger
256-258: Simplify nested if statementsThe nested if statements can be combined into a single condition for better readability.
-if settings.enable_input_validation: - if not content or not isinstance(content, str): - raise ValueError("Content must be a non-empty string") +if settings.enable_input_validation and (not content or not isinstance(content, str)): + raise ValueError("Content must be a non-empty string")src/agents_minimal.py (2)
1-1: Clarify the purpose and usage of the minimal implementationHaving two implementations (agents.py and agents_minimal.py) could cause confusion. Consider adding more detailed documentation about when to use each version.
-"""Minimal MCP Agent orchestrator for testing without external dependencies.""" +"""Minimal MCP Agent orchestrator for testing without external dependencies. + +This module provides a lightweight alternative to agents.py for: +- Unit testing without external dependencies +- Development environments where monitoring is not needed +- Standalone deployments with minimal requirements + +For production use, prefer agents.py which includes full monitoring and logging. +"""
263-265: Simplify nested if statementsSimilar to agents.py, the nested if statements can be combined.
-if settings.enable_input_validation: - if not content or not isinstance(content, str): - raise ValueError("Content must be a non-empty string") +if settings.enable_input_validation and (not content or not isinstance(content, str)): + raise ValueError("Content must be a non-empty string")
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📒 Files selected for processing (14)
DEPLOYMENT.md(1 hunks)Dockerfile(1 hunks)PRODUCTION_READY_SUMMARY.md(1 hunks)README.md(1 hunks)docker-compose.yml(1 hunks)monitoring/prometheus.yml(1 hunks)requirements.txt(1 hunks)src/agents.py(1 hunks)src/agents_minimal.py(1 hunks)src/api.py(1 hunks)src/config.py(1 hunks)src/models.py(1 hunks)src/monitoring.py(1 hunks)src/test_production.py(1 hunks)
🧰 Additional context used
🧬 Code Graph Analysis (5)
src/config.py (1)
src/agents_minimal.py (1)
Settings(10-23)
src/test_production.py (3)
src/models.py (2)
ProcessingRequest(9-21)ProcessingResponse(24-34)src/agents.py (6)
Orchestrator(244-282)DataParserAgent(32-63)SummarizerAgent(66-106)OptimizerAgent(109-142)process(21-25)run(252-282)src/monitoring.py (5)
MetricsCollector(43-100)MonitoringMiddleware(103-132)record_request(57-71)get_health_check(82-95)monitor_request(109-132)
src/monitoring.py (3)
src/models.py (2)
AgentMetrics(37-45)HealthCheck(48-57)src/api.py (1)
metrics(67-75)src/agents.py (1)
process(21-25)
src/models.py (1)
src/agents_minimal.py (2)
ProcessingRequest(29-33)ProcessingResponse(36-45)
src/agents_minimal.py (3)
src/config.py (1)
Settings(9-41)src/models.py (2)
ProcessingRequest(9-21)ProcessingResponse(24-34)src/agents.py (22)
BaseAgent(14-29)process(21-25)_process_impl(27-29)_process_impl(38-63)_process_impl(72-93)_process_impl(115-126)_process_impl(151-163)DataParserAgent(32-63)SummarizerAgent(66-106)_create_summary(95-106)OptimizerAgent(109-142)_optimize_content(128-142)LoggerAgent(145-163)AgentFactory(166-178)create_agents(170-178)LoadBalancer(181-241)distribute(188-241)Orchestrator(244-282)run(252-282)run_sync(286-293)_run(288-291)main(298-303)
🪛 YAMLlint (1.37.1)
monitoring/prometheus.yml
[error] 19-19: no new line character at the end of file
(new-line-at-end-of-file)
docker-compose.yml
[error] 72-72: no new line character at the end of file
(new-line-at-end-of-file)
🪛 Ruff (0.12.2)
src/config.py
3-3: os imported but unused
Remove unused import: os
(F401)
src/test_production.py
5-5: unittest.mock.patch imported but unused
Remove unused import
(F401)
5-5: unittest.mock.MagicMock imported but unused
Remove unused import
(F401)
6-6: json imported but unused
Remove unused import: json
(F401)
src/monitoring.py
7-7: typing.Any imported but unused
Remove unused import: typing.Any
(F401)
8-8: prometheus_client.CONTENT_TYPE_LATEST imported but unused
Remove unused import: prometheus_client.CONTENT_TYPE_LATEST
(F401)
125-125: Local variable success is assigned to but never used
Remove assignment to unused variable success
(F841)
src/api.py
3-3: asyncio imported but unused
Remove unused import: asyncio
(F401)
4-4: typing.Dict imported but unused
Remove unused import
(F401)
4-4: typing.Any imported but unused
Remove unused import
(F401)
5-5: fastapi.BackgroundTasks imported but unused
Remove unused import: fastapi.BackgroundTasks
(F401)
101-101: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
115-115: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
src/agents.py
256-257: Use a single if statement instead of nested if statements
(SIM102)
src/agents_minimal.py
173-173: Local variable log_data is assigned to but never used
Remove assignment to unused variable log_data
(F841)
263-264: Use a single if statement instead of nested if statements
(SIM102)
🪛 markdownlint-cli2 (0.17.2)
DEPLOYMENT.md
130-130: Bare URL used
(MD034, no-bare-urls)
134-134: Bare URL used
(MD034, no-bare-urls)
136-136: Bare URL used
(MD034, no-bare-urls)
🪛 LanguageTool
PRODUCTION_READY_SUMMARY.md
[uncategorized] ~1-~1: If this is a compound adjective that modifies the following noun, use a hyphen.
Context: # MCP Agent Stack - Production Ready Summary ## 🎯 Mission Accomplished Th...
(EN_COMPOUND_ADJECTIVE_INTERNAL)
🔇 Additional comments (13)
requirements.txt (1)
7-8: Double-check FastAPI ↔︎ Pydantic v2 compatibilityFastAPI gained stable Pydantic-v2 support only from
0.104. Verify that every other lib (e.g.fastapi-utils, if you later add it) and your ownpydantic_settingsusage are compatible with Pydantic v2, otherwise downgradepydanticto<2or upgrade the affected libs.Dockerfile (3)
1-23: Well-structured multi-stage build setup!The builder stage follows Docker best practices with appropriate Python environment variables, minimal build dependencies, and proper cleanup of apt lists.
24-50: Excellent security practices in production stage!The implementation properly follows security best practices with non-root user creation, minimal runtime dependencies, and proper file ownership.
58-60: Verify health check start period is sufficient.The 5-second start period might be too short if the application has heavy initialization (loading models, establishing connections, etc.). Consider increasing it if you observe false-positive health check failures during container startup.
src/test_production.py (4)
14-80: Comprehensive agent test coverage!The tests thoroughly cover agent functionality, validation, and configuration scenarios. Good use of try/finally to ensure settings are restored.
127-169: Well-designed monitoring tests!Good coverage of both success and error scenarios for metrics collection and monitoring middleware.
171-199: Thorough model validation tests!Tests properly validate the content size limit and model instantiation.
218-257: Excellent integration test coverage!The tests effectively validate end-to-end processing and concurrent request handling, ensuring the system works correctly under real-world conditions.
PRODUCTION_READY_SUMMARY.md (1)
1-239: Excellent production readiness documentation!This comprehensive summary effectively documents all production features, deployment options, and monitoring capabilities. The before/after comparison and future enhancements section provide valuable context.
src/monitoring.py (2)
43-101: Well-implemented metrics collection!The MetricsCollector properly integrates Prometheus metrics with internal tracking, providing comprehensive observability.
135-137: Appropriate singleton pattern for monitoring components.Global instances are suitable for application-wide monitoring infrastructure.
src/models.py (2)
37-45: Well-structured metrics model!The AgentMetrics model appropriately captures all necessary performance data with sensible defaults.
48-57: Comprehensive health check model!The HealthCheck model captures all essential system health indicators.
| @@ -0,0 +1,45 @@ | |||
| """Configuration management for MCP Agent Stack.""" | |||
|
|
|||
| import os | |||
There was a problem hiding this comment.
Remove unused import
os is imported but not used, triggering Ruff F401 and adding dead code.
-import os📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| import os |
🧰 Tools
🪛 Ruff (0.12.2)
3-3: os imported but unused
Remove unused import: os
(F401)
🤖 Prompt for AI Agents
In src/config.py at line 3, the 'os' module is imported but not used anywhere in
the file, causing a linting warning for an unused import. Remove the line
importing 'os' to eliminate dead code and resolve the Ruff F401 warning.
| test: ["CMD", "curl", "-f", "http://localhost:8000/health"] | ||
| interval: 30s | ||
| timeout: 10s | ||
| retries: 3 |
There was a problem hiding this comment.
Ensure curl exists in the runtime image
The health-check relies on curl, but many slim Python images omit it. Either:
- Install
curlin the final stage of the Dockerfile, or - Replace with
CMD-SHELL+wget --spiderwhich is typically present.
Failing to do so will flip the container to unhealthy.
🤖 Prompt for AI Agents
In docker-compose.yml around lines 24 to 27, the health-check uses curl which
may not be present in the slim Python runtime image, causing the container to be
marked unhealthy. Fix this by either modifying the Dockerfile to install curl in
the final image stage or change the health-check command to use CMD-SHELL with
wget --spider instead, as wget is more commonly included in such images.
| import pytest | ||
| import asyncio | ||
| from unittest.mock import patch, MagicMock | ||
| import json |
There was a problem hiding this comment.
Remove unused imports.
Static analysis correctly identified unused imports that should be removed.
import pytest
import asyncio
-from unittest.mock import patch, MagicMock
-import json📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| import pytest | |
| import asyncio | |
| from unittest.mock import patch, MagicMock | |
| import json | |
| import pytest | |
| import asyncio |
🧰 Tools
🪛 Ruff (0.12.2)
5-5: unittest.mock.patch imported but unused
Remove unused import
(F401)
5-5: unittest.mock.MagicMock imported but unused
Remove unused import
(F401)
6-6: json imported but unused
Remove unused import: json
(F401)
🤖 Prompt for AI Agents
In src/test_production.py around lines 3 to 6, some imported modules are not
used in the code. Review the imports and remove any that are not referenced
anywhere in the file to clean up the code and avoid unnecessary dependencies.
| @pytest.mark.asyncio | ||
| async def test_orchestrator_error_handling(self): | ||
| """Test error handling in orchestrator.""" | ||
| orchestrator = Orchestrator() | ||
|
|
||
| # Test with oversized content | ||
| large_content = "x" * (settings.max_input_size + 1) | ||
| response = await orchestrator.run(large_content) | ||
|
|
||
| assert response.success is False | ||
| assert response.error_message is not None | ||
| assert "Data too large" in response.error_message |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify error handling for oversized content.
This test expects the orchestrator to return a response with success=False for oversized content. However, the ProcessingRequest model validator will raise a ValueError for content > 10,000 characters. Ensure the orchestrator properly catches this validation error and returns an error response instead of raising an exception.
🏁 Script executed:
#!/bin/bash
# Check how the orchestrator handles validation errors
ast-grep --pattern 'class Orchestrator {
$$$
async def run($$$) {
$$$
}
$$$
}'
# Also check for try-except blocks handling ProcessingRequest creation
rg -A 10 "ProcessingRequest\(" src/agents.pyLength of output: 568
🏁 Script executed:
#!/bin/bash
# Locate the Orchestrator class and inspect its run method for exception handling
rg -n "class Orchestrator" -A 50 --glob '*.py' srcLength of output: 6210
Handle validation errors in Orchestrator.run by returning an error response instead of re-raising
The current implementation catches all exceptions and re-raises them, so a ValueError from ProcessingRequest (e.g. oversized content) will bubble up instead of producing a ProcessingResponse(success=False, …). Update the except block in src/agents.py (and likewise in src/agents_minimal.py if needed) to catch validation errors and return a properly populated error response:
• Location: src/agents.py, around lines 280–283
• Suggested change:
- except Exception as e:
- self.logger.error("Orchestration failed", error=str(e))
- raise
+ except ValueError as e:
+ # Return a failure response for input‐validation errors
+ self.logger.error("Validation error during orchestration", error=str(e))
+ return ProcessingResponse(
+ success=False,
+ error_message=str(e),
+ processed_content=None,
+ processing_time_ms=0
+ )
+ except Exception as e:
+ # Re-raise unexpected errors
+ self.logger.error("Orchestration failed", error=str(e))
+ raiseThis ensures that when content exceeds settings.max_input_size, the orchestrator returns a failure response with success=False and an appropriate error_message, satisfying the test’s expectations.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| @pytest.mark.asyncio | |
| async def test_orchestrator_error_handling(self): | |
| """Test error handling in orchestrator.""" | |
| orchestrator = Orchestrator() | |
| # Test with oversized content | |
| large_content = "x" * (settings.max_input_size + 1) | |
| response = await orchestrator.run(large_content) | |
| assert response.success is False | |
| assert response.error_message is not None | |
| assert "Data too large" in response.error_message | |
| try: | |
| # existing orchestration logic | |
| except ValueError as e: | |
| # Return a failure response for input‐validation errors | |
| self.logger.error("Validation error during orchestration", error=str(e)) | |
| return ProcessingResponse( | |
| success=False, | |
| error_message=str(e), | |
| processed_content=None, | |
| processing_time_ms=0 | |
| ) | |
| except Exception as e: | |
| # Re-raise unexpected errors | |
| self.logger.error("Orchestration failed", error=str(e)) | |
| raise |
🤖 Prompt for AI Agents
In src/agents.py around lines 280 to 283, the except block currently re-raises
all exceptions, causing validation errors like ValueError from ProcessingRequest
to propagate instead of returning a failure response. Modify this except block
to catch validation errors specifically and return a ProcessingResponse with
success set to False and an appropriate error_message describing the validation
failure. Apply the same fix to src/agents_minimal.py if it has a similar
pattern. This change will ensure that oversized content triggers a proper error
response instead of an unhandled exception.
| from typing import Dict, Any | ||
| from prometheus_client import Counter, Histogram, Gauge, generate_latest, CONTENT_TYPE_LATEST |
There was a problem hiding this comment.
Remove unused imports.
Static analysis correctly identified unused imports.
from datetime import datetime
-from typing import Dict, Any
+from typing import Dict
from prometheus_client import Counter, Histogram, Gauge, generate_latest, CONTENT_TYPE_LATEST
+from prometheus_client import Counter, Histogram, Gauge, generate_latestCommittable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff (0.12.2)
7-7: typing.Any imported but unused
Remove unused import: typing.Any
(F401)
8-8: prometheus_client.CONTENT_TYPE_LATEST imported but unused
Remove unused import: prometheus_client.CONTENT_TYPE_LATEST
(F401)
🤖 Prompt for AI Agents
In src/monitoring.py around lines 7 to 8, remove any imports that are not used
in the file. Specifically, check if all imported items from prometheus_client
are utilized; if some like Histogram, Gauge, or generate_latest,
CONTENT_TYPE_LATEST are unused, delete those imports to clean up the code.
| except Exception as e: | ||
| logger.error("Processing failed", | ||
| request_id=request.request_id, | ||
| error=str(e)) | ||
| raise HTTPException(status_code=500, detail=str(e)) |
There was a problem hiding this comment.
Improve exception handling with proper chaining
When re-raising exceptions in an except block, use raise ... from e to preserve the exception chain.
except Exception as e:
logger.error("Processing failed",
request_id=request.request_id,
error=str(e))
- raise HTTPException(status_code=500, detail=str(e))
+ raise HTTPException(status_code=500, detail=str(e)) from e📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| except Exception as e: | |
| logger.error("Processing failed", | |
| request_id=request.request_id, | |
| error=str(e)) | |
| raise HTTPException(status_code=500, detail=str(e)) | |
| except Exception as e: | |
| logger.error("Processing failed", | |
| request_id=request.request_id, | |
| error=str(e)) | |
| raise HTTPException(status_code=500, detail=str(e)) from e |
🧰 Tools
🪛 Ruff (0.12.2)
101-101: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling
(B904)
🤖 Prompt for AI Agents
In src/api.py around lines 97 to 101, the exception is re-raised without
preserving the original exception context. Modify the raise statement to use
'raise HTTPException(...) from e' to maintain proper exception chaining and
improve error traceability.
| # Add CORS middleware | ||
| app.add_middleware( | ||
| CORSMiddleware, | ||
| allow_origins=["*"], # Configure appropriately for production |
There was a problem hiding this comment.
Configure CORS appropriately for production
The CORS middleware is configured to allow all origins, which is a security risk in production environments.
Consider implementing environment-specific CORS configuration:
+# Add to config.py:
+# allowed_origins: List[str] = Field(
+# default=["http://localhost:3000"],
+# description="Allowed CORS origins"
+# )
app.add_middleware(
CORSMiddleware,
- allow_origins=["*"], # Configure appropriately for production
+ allow_origins=settings.allowed_origins if not settings.debug else ["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In src/api.py at line 27, the CORS middleware is currently set to allow all
origins with allow_origins=["*"], which is insecure for production. Modify this
to use environment-specific configuration by reading allowed origins from
environment variables or a config file, and set allow_origins accordingly to
restrict access only to trusted domains in production environments.
| # Step 1: Parse data | ||
| self.logger.info("Starting data parsing", request_id=request.request_id) |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Verify agent indexing matches the pipeline steps
The code assumes specific agent positions in the list (e.g., agents[0] for DataParserAgent). This tight coupling could break if the agent order changes.
🏁 Script executed:
#!/bin/bash
# Description: Verify that AgentFactory creates agents in the expected order
# Search for AgentFactory.create_agents implementation
ast-grep --pattern 'def create_agents() -> List[BaseAgent]:
$$$
return [
$$$
]'
# Also check for any other usages of agent indices
rg -A 5 'self\.agents\[\d+\]' src/Length of output: 4341
🏁 Script executed:
#!/bin/bash
sed -n '150,220p' src/agents.pyLength of output: 2808
Fix agent indexing mismatch in LoadBalancer.distribute
The sequence returned by AgentFactory.create_agents no longer aligns with the hard-coded indices in LoadBalancer.distribute, causing misrouted calls and an out-of-range access:
- In
src/agents.py:172–178,create_agentsnow returns only these five agents:DataParserAgentSummarizerAgentLoggerAgentOptimizerAgentLoggerAgent
- In
LoadBalancer.distribute(~lines 186–214):self.agents[1].process(…)is meant to log parsing completion but actually invokesSummarizerAgent.self.agents[2].process(…)is used for summarization but invokesLoggerAgent, sosummarybecomesNone.- Later,
self.agents[5].process(…)for final logging will throw anIndexError(only indices 0–4 exist).
Either restore the original 6-agent order (inserting LoggerAgent at positions 1 and 3) or adjust all self.agents[...] references to match the current factory output.
🤖 Prompt for AI Agents
In src/agents.py around lines 186 to 214, the LoadBalancer.distribute method
uses hard-coded agent indices that no longer match the five-agent list returned
by AgentFactory.create_agents (lines 172 to 178). This causes misrouted calls
and an IndexError. To fix this, update all self.agents[...] references in
distribute to use indices 0 to 4 corresponding to the current agents:
DataParserAgent (0), SummarizerAgent (1), LoggerAgent (2), OptimizerAgent (3),
and LoggerAgent (4). Ensure each process call targets the correct agent by
adjusting the indices accordingly.
| async def _process_impl(self, message: str) -> None: | ||
| """Log message with enhanced context.""" | ||
| log_data = { | ||
| "message": message, | ||
| "timestamp": datetime.utcnow().isoformat(), | ||
| "log_level": "INFO" | ||
| } | ||
|
|
||
| print(f"[{self.name.upper()}] {message}") |
There was a problem hiding this comment.
Remove unused variable assignment
The log_data variable is created but never used, only its contents are used in the print statement.
async def _process_impl(self, message: str) -> None:
"""Log message with enhanced context."""
- log_data = {
- "message": message,
- "timestamp": datetime.utcnow().isoformat(),
- "log_level": "INFO"
- }
-
- print(f"[{self.name.upper()}] {message}")
+ timestamp = datetime.utcnow().isoformat()
+ print(f"[{self.name.upper()}] [{timestamp}] {message}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| async def _process_impl(self, message: str) -> None: | |
| """Log message with enhanced context.""" | |
| log_data = { | |
| "message": message, | |
| "timestamp": datetime.utcnow().isoformat(), | |
| "log_level": "INFO" | |
| } | |
| print(f"[{self.name.upper()}] {message}") | |
| async def _process_impl(self, message: str) -> None: | |
| """Log message with enhanced context.""" | |
| timestamp = datetime.utcnow().isoformat() | |
| print(f"[{self.name.upper()}] [{timestamp}] {message}") |
🧰 Tools
🪛 Ruff (0.12.2)
173-173: Local variable log_data is assigned to but never used
Remove assignment to unused variable log_data
(F841)
🤖 Prompt for AI Agents
In src/agents_minimal.py around lines 171 to 179, the variable log_data is
assigned but never used. Remove the log_data dictionary assignment entirely
since only its contents are used in the print statement, which can remain
unchanged.
| LoggerAgent(), # Log parsing start | ||
| SummarizerAgent(), | ||
| LoggerAgent(), # Log summarization | ||
| OptimizerAgent(), | ||
| LoggerAgent() # Final logging |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Fix inconsistent agent order compared to agents.py
The agent order differs from the main implementation in agents.py. The LoggerAgent positions are different, which could cause confusion when switching between implementations.
The AgentFactory in this file creates agents in a different order than in agents.py. This should be consistent:
return [
DataParserAgent(),
- LoggerAgent(), # Log parsing start
SummarizerAgent(),
- LoggerAgent(), # Log summarization
+ LoggerAgent(),
OptimizerAgent(),
- LoggerAgent() # Final logging
+ LoggerAgent() # Final logging step
]Let me verify the exact order in both files:
🏁 Script executed:
#!/bin/bash
# Compare agent creation order between both files
echo "=== agents.py AgentFactory ==="
rg -A 10 "def create_agents.*:" src/agents.py
echo -e "\n=== agents_minimal.py AgentFactory ==="
rg -A 10 "def create_agents.*:" src/agents_minimal.pyLength of output: 931
Align agent order in src/agents_minimal.py with src/agents.py
The create_agents sequence in agents_minimal.py should mirror the main implementation in agents.py to avoid confusion:
• File: src/agents_minimal.py
Update the returned list in def create_agents() as follows:
@@ def create_agents() -> List[BaseAgent]:
return [
DataParserAgent(),
- LoggerAgent(), # Log parsing start
SummarizerAgent(),
- LoggerAgent(), # Log summarization
OptimizerAgent(),
- LoggerAgent() # Final logging
+ LoggerAgent() # Final logging step
]📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| LoggerAgent(), # Log parsing start | |
| SummarizerAgent(), | |
| LoggerAgent(), # Log summarization | |
| OptimizerAgent(), | |
| LoggerAgent() # Final logging | |
| def create_agents() -> List[BaseAgent]: | |
| return [ | |
| DataParserAgent(), | |
| SummarizerAgent(), | |
| OptimizerAgent(), | |
| LoggerAgent() # Final logging step | |
| ] |
🤖 Prompt for AI Agents
In src/agents_minimal.py around lines 190 to 194, the order of agents returned
by create_agents() does not match the sequence in src/agents.py, which can cause
confusion. Update the list of agents in create_agents() to exactly mirror the
order found in src/agents.py, ensuring the sequence and placement of LoggerAgent
and other agents are consistent between both files.
Transform MCP Agent Stack into a production-ready system to enable live deployment with enhanced features and robust operations.
This PR introduces async processing, comprehensive monitoring (Prometheus, structured logging, health checks), security hardening (input validation, non-root containers), containerization (Docker, Docker Compose), a FastAPI web server, and a comprehensive testing suite, along with a detailed deployment guide.
Summary by CodeRabbit
New Features
Bug Fixes
Tests