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    Enables fast, low-token code search for AI coding agents via a local BM25 engine built on SQLite FTS5, with support for camelCase, snake_case, and Japanese text. Provides a stateless MCP stdio server and a Hermes adapter for multi-agent environments.
    1
    MIT
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    Enables semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.
    1
    MIT
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    Semantic + lexical code search as an MCP server. Agents query in natural language and get back ranked file:line ranges to read precisely.
    8 npm
    1
    MIT
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    A paid remote MCP server for routing code search queries, providing structured receipts, audit logs, and reviewer-ready evidence.
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    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.
    8
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  • A
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    MCP server with local vector search for your codebase. Smart indexing, semantic search, Git history — all offline.
    7
    94 PyPI
    49
    MIT
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    A FastMCP server for bounded, keyword-driven repository context search, enabling efficient codebase exploration with snippet extraction and focused code block retrieval.
    2
    GPL 3.0
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    Helps AI assistants optimize Linux workloads on Arm64 by parsing perf report output, recommending NEON SIMD intrinsics for hot loops, and auditing Python dependency manifests for arm64 wheel availability — all offline and structured.
    4
    Apache 2.0
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    Provides a code search service via MCP, allowing AI clients to perform fast, indexed searches across large codebases using Zoekt, with support for filters like repository, language, and path.
    2
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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.
    3
    3
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