Fraim Context MCP

# Fraim Context MCP > Semantic search MCP server for project documentation. **Version**: 5.1.0 **Status**: In Development ## Overview Fraim Context MCP exposes project documentation to LLMs via the Model Context Protocol (MCP). It supports: - **Fast mode**: Direct cache/search for immediate results - **Deep mode**: Multi-round synthesis for complex queries - **Hybrid search**: Vector similarity + full-text search with pgvector - **Smart caching**: Redis with corpus versioning for cache invalidation ## Quick Start ```bash # 1. Setup Doppler doppler login doppler setup # Select: fraim-context → dev # 2. Install dependencies uv sync # 3. Verify environment doppler run -- uv run python scripts/verify_env.py # 4. Run tests doppler run -- uv run pytest tests/stage_0/ -v ``` ## Development This project uses **Test-Driven Development (TDD)**. See `DNA/DEVELOPMENT_PLAN.md` for stages. ```bash # Run all tests doppler run -- uv run pytest tests/ -v # Run specific stage doppler run -- uv run pytest tests/stage_0/ -v # Lint uv run ruff check src/ tests/ # Type check uv run mypy src/fraim_mcp ``` ## Architecture - **LLM Access**: Pydantic AI Gateway (unified key for all providers) - **Database**: PostgreSQL + pgvector (1024-dim embeddings) - **Cache**: Redis 7.x (native asyncio) - **Observability**: Logfire (OpenTelemetry) See `DNA/specs/ARCHITECTURE.md` for full details. ## License MIT