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.
Enables AI assistants to access and search MkDocs documentation through tools for full-text search, page navigation, and code block extraction. It serves documentation pages as readable resources and provides structural outlines to help LLMs navigate documentation content.
Enables LLMs to perform high-performance code search and analysis across multiple languages using symbol indexing, regex text search, and structural AST pattern matching. It also provides tools for technology stack detection and dependency analysis with persistent caching for optimized performance.
Enables coding agents to query local notes, decisions, docs, and code with hybrid retrieval (BM25 + embeddings + reranking) and get path:line citations. It provides tools like rag_query for full-corpus search and search_knowledge for project-scoped knowledge recall.
Enables access to scientific simulation documentation and code snippets for simulators like PyBaMM and Cantera. Provides AI-powered search and retrieval of simulation examples and documentation through natural language queries.