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213,217 tools. Last updated 2026-06-19 13:08

"A tool for searching code using semantic understanding" matching MCP tools:

  • Find code by meaning using semantic search with natural language queries. Combines AI understanding with text matching to locate relevant snippets, handling typos and variations.
    MIT
  • Search indexed code and documentation using hybrid semantic and keyword retrieval. Ideal for answering questions about your codebase and project architecture.
    Apache 2.0
  • Search a local semantic index using natural language queries to retrieve conceptually related code snippets.

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  • Search and retrieve stored memories using semantic understanding, filter by source type or bucket, and sort results by date or relevance for precise information retrieval.
    MIT
  • Create a vector index for semantic code search by generating embeddings with tree-sitter and Jina AI, enabling efficient and accurate querying of source code files.
  • Find code by meaning using combined semantic and structural search. Locate features based on natural language descriptions without exact keywords.
    MIT
  • Retrieve metadata for a specific Code Ocean capsule using its unique ID. Use this tool to fetch detailed information about known computational workflows and data assets.
    MIT
  • Add documents to a collection by providing a URL for download, processing them for text extraction, and indexing them for semantic search.
    MIT
  • Search documents using semantic understanding to find relevant content based on meaning rather than keywords. Understands natural language queries and returns ranked passages with source information.
    MIT
  • Retrieve a hierarchical table of contents of the Municipal Code to discover available sections before searching. Optionally filter by chapter for targeted navigation.
    MIT
  • Create a new document collection to organize related documents and enable semantic search across their contents. Returns a collection ID for managing documents.
    MIT
  • Retrieve AI-generated answers by searching your namespace of text documents or using direct AI model calls for question answering.
    Apache 2.0
  • Search persistent memory using semantic similarity to retrieve relevant memories and related knowledge graph facts.
    MIT