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Incrementally index repos and documents, then run semantic search over them.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "cocoindex MCP" yet — see the docs or source repo.
Use cocoindex MCP to incrementally index our Git repositories into Postgres + pgvector, and show me how to search functions, modules, and implementation examples in natural language.
Provides the indexing workflow, required configuration, and ready-to-use semantic search example queries.
Incrementally index project docs, design notes, and READMEs into the vector store, then help me find configuration guidance and examples related to user permissions.
Returns relevant document snippets, matched sources, and semantic search results about permission configuration.
After code and docs change, use cocoindex MCP to run an incremental update and verify that the newly added API documentation is discoverable via semantic search.
Outputs the incremental update status, a summary of indexed content, and verification results showing whether new content is searchable.
Retrieve and understand code faster with hybrid RAG search across repositories.
Build semantic codebase indexes so AI can search and navigate projects faster.
Index local repositories for semantic search and structured code understanding.
Build semantic indexes for codebases to find relevant code with natural language queries.
Index local Git repos for fast code search and snippet retrieval.
Build persistent, semantically searchable memory for codebases via natural language queries.