Enables AI assistants to fetch, index, and perform semantic RAG-based searches on API documentation from various sources. It provides tools for hybrid search and collection management, allowing users to access up-to-date documentation from projects like Gemini and FastMCP.
Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
Enables developers to ask natural-language questions and receive grounded answers sourced from official AI and Python library documentation, with automatic search, extraction, and source preservation.
Enables AI assistants to explore, search, and read codebase repositories and API specifications efficiently, with support for file searching, content search via ripgrep, and reading API specs.
