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    An MCP server for intelligently reading Java source code, supporting extraction from Maven dependencies and local projects with dual decompilers.
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    41 PyPI
    154
    Apache 2.0
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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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    Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.
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    BSD 2-Clause "Simplified"
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    A self-hosted semantic code retrieval MCP server that enables natural language queries to find relevant code snippets using Tree-sitter AST parsing, embedding, and hybrid search with reranking.
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    MIT
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    Local semantic recall MCP server for citation-integrity workflows. Enables semantic search over a text corpus, returning file paths and page numbers for verification, with 100% local processing using ONNX embeddings and LanceDB.
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    MIT
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    Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.
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    AGPL 3.0
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    Enables AI agents to perform semantic search over codebases by converting natural language queries into efficient search patterns like grep and ripgrep. It utilizes LLMs to verify relevance and find code snippets that traditional keyword-based searches might miss.
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    An MCP server that lets users find nearby shops and POIs around a location and find targets such as gas stations or charging points within a given radius along a driving route, using the Amap (Gaode) Web Service API with built-in API key entry and validation.
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    10 npm
    MIT
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    Enables AI coding assistants to semantically search codebases by meaning rather than exact text, with zero external daemons and fully local embeddings. Works out-of-the-box with Claude Code, Gemini CLI, Antigravity, and Cursor.
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    MIT
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    Enables Claude Cowork on Windows to read, write, edit, list, find, and regex-search files inside a WSL distribution over stdio, accepting Windows and Linux path spellings. Access is confined to server-fixed roots, with secrets files refused, key-shaped strings redacted, and no delete or move operations.
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    MIT
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    Enables AI coding agents to perform fast regex/text and structural AST searches across codebases, find symbols, list files, and safely preview or apply refactors. It unifies ripgrep and ast-grep with pagination, gitignore-aware defaults, and fallback engines for zero-setup code search.
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    28 npm
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    MIT
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    MCP server for administering wg-easy (WireGuard Easy) instances, enabling listing, creating, updating, enabling/disabling, and deleting WireGuard clients, fetching configs and QR codes, and inspecting server status via the wg-easy REST API.
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    84 npm
    MIT
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    Enables semantic search over local markdown specs, RFCs, and docs using Ollama and LanceDB, allowing AI agents to find relevant information by meaning rather than exact keywords.
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    Self-hosted semantic code search for AI coding agents. Indexes many repositories at once with tree-sitter AST chunking, local llama.cpp embeddings and Qdrant, so Claude Code, Cursor, Codex and any MCP client can find code by meaning, trace usages and callers, and read the git history behind each chunk. No API keys, and your code never leaves your machine.
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    MIT
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    A local stdio MCP server for managing files on XServer via SFTP, enabling secure file operations, backups, and workspace management for XServer hosting.
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    MIT
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    Generates test files locally (images, PDFs, CSVs, corrupted files, etc.) from natural language prompts, with configurable sizes and safety limits.
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    MIT
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    A lightweight MCP server for basic file operations, enabling reading, writing, and listing files securely via the Model Context Protocol.
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    Publishes a spending-summary tool alongside an interactive HTML view, letting people click a category in the answer to fetch the individual expense records behind that total. Keeps calculations and their supporting evidence together in a single inspectable flow.
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    MCP file cache for AI coding agents. Re-reading an unchanged file costs ~40 tokens instead of the whole file; a changed file comes back as a diff. Adds grep, BM25 search and glob over cached files, atomic edits with dry_run, and token-budgeted batch reads. One local daemon serves every client. No API keys, no GPU. Works with Claude Code, Cursor, VS Code, Codex and OpenCode.
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    76 PyPI
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    MIT