float-mcp

--- title: Research Session query: 'swarm-development-centralized-1751992067817/qa/procedures' date: 7/8/2025 time: 12:37:55 PM timestamp: 2025-07-08T16:37:55.393Z exchanges: 1 --- # Research Session ## Initial Query **Question:** swarm-development-centralized-1751992067817/qa/procedures **Response:** **QA procedures for a centralized Float MCP server using FastMCP should combine in-memory, integration, and end-to-end testing, leveraging both automated and manual validation.** --- ### Core QA Procedures **1. In-Memory and Unit Testing (FastMCP Pattern)** - Use FastMCP’s ability to pass the server instance directly to a test client for fast, isolated, in-memory tests. - Write pytest-based tests for each registered tool/resource, validating both expected outputs and error handling. - Example pattern: ```python import pytest from fastmcp import FastMCP, Client @pytest.fixture def mcp_server(): server = FastMCP("TestServer") @server.tool def greet(name: str) -> str: return f"Hello, {name}!" return server async def test_greet_tool(mcp_server): async with Client(mcp_server) as client: result = await client.call_tool("greet", {"name": "World"}) assert result.data == "Hello, World!" ``` - Use Python’s mocking and patching tools to simulate Float API responses, rate limiting, and error scenarios[3]. **2. Integration and End-to-End Testing** - Start the MCP server in a controlled environment (local or Docker). - Use a test client (requests, FastMCP Client, or custom scripts) to send real HTTP requests to all endpoints. - Validate: - Correct response schemas (using Pydantic or Zod) - Authentication and permission checks - Error handling for invalid input, missing fields, and simulated API failures - Pagination and rate limiting logic - For Float API integration, mock or use a sandbox API key to avoid affecting production data[1][3]. **3. Manual and GUI-Based Testing** - Use tools like Postman, curl, or MCP Inspector GUI to manually trigger endpoints and visually inspect responses and logs[4]. - Validate that: - All endpoints are reachable and return valid JSON - Logs are generated for each request with timestamps and outcomes - No sensitive data or system internals are exposed in errors or logs[2][4] **4. Security and Production Readiness** - Enforce schema validation for all incoming tasks and API requests. - Sanitize all inputs to prevent injection attacks. - Implement and test rate limiting (simulate 429 responses). - Ensure all logs redact sensitive data and include context (request ID, user, etc.). - If deploying publicly, verify TLS/HTTPS is enforced[2]. **5. Performance and Resource Monitoring** - Use automated scripts or tools (e.g., Locust, Apache Bench) to simulate concurrent requests and measure: - Response times (target <2000ms) - Memory usage (target <200MB) - Server stability under load[1] --- ### Actionable Steps for This Project - **Automate**: Use pytest for all unit/integration tests, leveraging FastMCP’s in-memory testing for speed and coverage[3]. - **Validate**: Use the response-validator.ts logic as a reference to implement Pydantic-based schema checks in Python. - **Monitor**: Integrate logging and performance monitoring as described in your Logging and Monitoring task. - **Document**: Update README and QA documentation to include all test procedures, expected results, and troubleshooting steps. - **Pre-Deployment**: Require all tests (unit, integration, E2E, Docker) to pass before deployment, and review logs for anomalies. --- **References to your project files and tasks:** - Implement these procedures in `tests/qa/` (pytest for Python, Docker for deployment tests). - Use `float_api` mocks for integration tests. - Document all procedures in `tests/qa/README.md` and ensure they align with your QA checklist and deployment validation requirements. --- _Generated by Task Master Research Command_ _Timestamp: 2025-07-08T16:37:55.393Z_