# Implementation Summary
## Architecture Overview
- **Backend**: FastAPI application exposing REST and Model Context Protocol (MCP) interfaces. Tools persist execution artefacts to Neo4j.
- **Persistence**: Neo4j 5.x drives graph storage for lint/test/execution artefacts and user-defined nodes.
- **MCP Layer**: FastMCP server mounted under `/mcp`, registering lint, test, execution, generation, and graph tools.
- **Agent Client**: Python integration that discovers tools and connects the OpenAI Agents SDK to the MCP server.
- **Frontend**: React + TypeScript (Vite) single-page app. Provides code editor, tooling controls, and graph metrics dashboard.
- **Orchestration**: Docker Compose coordinates backend (non-root Python), frontend (nginx-unprivileged), and Neo4j. Security hardening drops Linux capabilities and enforces explicit environment variables.
- **CI/CD**: GitHub Actions pipeline installs dependencies, runs lint/type checks, executes tests with coverage, builds Docker images, and publishes coverage artefacts.
## Data Flow
1. **User Interaction**: Frontend posts payloads (lint, execute, graph) to backend REST endpoints.
2. **Request Handling**: FastAPI enforces auth, rate limits, request size, and logs every call with a unique request ID.
3. **Tool Execution**:
- Linting runs AST analysis and external linters (Ruff/Flake8) when available.
- Testing spins up isolated pytest runs.
- Execution pipelines code through a sandboxed Python subprocess.
- Generation renders Jinja2 templates.
- Graph operations persist nodes/relationships in Neo4j.
4. **Persistence**: Results stored as labelled nodes (`LintResult`, `TestResult`, etc.) with metadata and timestamps. Graph analytics leverage Cypher queries for metrics.
5. **Metrics**: `/metrics` aggregates counts and degree distributions from Neo4j to feed the UI.
6. **MCP**: The same tool implementations back MCP tool handlers, enabling AI agents to reuse the underlying logic.
## Key Modules
- `mcp_server/server.py`: Application factory, middleware, security, route definitions, MCP registration.
- `mcp_server/tools/*`: Tool implementations and Pydantic request/response models.
- `mcp_server/database/neo4j_client.py`: Async driver wrapper, schema management, metrics.
- `agent_integration/client.py`: Agent discovery helpers and OpenAI Agent bridge.
- `frontend/src/*`: React components, hooks, and API client for REST endpoints.
- `scripts/*`: Developer automation (environment setup, smoke test).
## Testing Strategy
- **Unit Tests**: Exercise tool logic and validation (`test_tools.py`).
- **API Tests**: Hit REST endpoints against a live Neo4j container via HTTPX (`test_mcp_server.py`).
- **Integration Tests**: Launch uvicorn, run AgentDiscovery through FastMCP HTTP (`test_integration.py`).
- **Smoke Script**: `scripts/smoke_test.py` ensures health, metrics, and lint endpoints respond.
Minimum coverage enforced at 80% via pytest-cov.
## Extensibility Notes
- Additional tools plug into MCP by extending the tool registry and updating REST endpoints.
- Neo4j schema constraints already established for result nodes; new artefacts can follow the pattern.
- Rate limiting uses SlowAPI; global/default limits adjustable via `RATE_LIMIT_RPS`.
- Security middleware centralises headers, request IDs, CORS, and auth.
