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

# 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.

# 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