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109,613 tools. Last updated 2026-04-18 01:46
  • Analyze a code snippet for quality issues and semantic drift
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  • Search TED notices using semantic vector search. Best for conceptual queries and finding related tenders. Args: query: The search query (natural language) limit: Maximum number of results (default: 10) country: Filter by country code (optional) Returns: Search results ranked by semantic similarity
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  • Perform semantic search using vector embeddings with FAISS acceleration for better conceptual matching.
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  • Search saved contexts by keyword or natural language query. Returns matching context summaries ranked by relevance using hybrid keyword + semantic search. Searches across titles, reasoning, code snippets, file paths, and commit SHAs. Results include local contexts and (if authenticated) cloud workspace contexts including team-shared contexts. Use for finding past decisions, understanding why code was written, or discovering relevant prior work. Use gitwhy_get <context_id> to read the full context. CLI alternative: `git why search "<query>"`.
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    A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
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