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    A persistent, self-organizing memory MCP server for AI assistants, using semantic search, knowledge graphs, and reinforcement learning to automatically manage and retrieve memories.
    13
    MIT
  • A
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    A
    maintenance
    Skill librarian for coding agents: describe a task and get the few matching skills from a large local skill collection, then report whether they worked. Runs locally with Ollama and SQLite.
    42
    MIT
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    Enables AI clients to search curated, cited knowledge bases built from founders', CEOs', and investors' public talks, essays, and posts, returning passages with source links and dates. It also exposes available source collections via MCP tools so users can ask how those individuals might approach business, product, or growth questions.
    92
    MIT
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    Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.
    MIT
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    Your coding agent remembers how it solved past tasks and reuses it next time: trigger, steps, outcome, lessons. Hooks recall the right skill before a task; skills strengthen with confirmed use and unused ones decay. Local SQLite, no API key, nothing leaves your machine. 9 MCP tools, works with Claude Code, Codex and Cursor. Agent-written memory stays untrusted until you approve it.
    9
    53 PyPI
    11
    Apache 2.0
  • A
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    quality
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    Enables semantic search across documentation stored in Gemini FileSearchStores, returning AI-generated answers with source citations.
    1
    1
    MIT
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    The Search MCP Server enables seamless integration of network and local search capabilities in tools like Claude Desktop and Cursor, utilizing the Brave Search API for high-concurrency and asynchronous requests.
    1
    78
    MIT
  • A
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    Federated, local-first search for AI agents: one query fans across transcripts, files, a knowledge graph, a vector store, the live web, and YouTube, fused by trust-weighted RRF into one ranked answer. Five modes (grep/lexical/semantic/hybrid/rerank), runs entirely on your machine over MCP.
    6
    83 PyPI
    28
    Apache 2.0
  • A
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    Enables AI agents to search local Markdown documents using natural language, with automatic indexing and section-level retrieval.
    10
    6 npm
    1
    MIT
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    quality
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    MCP search and evidence tool for AI agents. Rewrites queries, zooms into source domains, and returns sourced answers with metrics.
    1
    4
    MIT
  • A
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    MCP server for hybrid documentation search (keyword + vector) and RAG chat, enabling natural language queries with cited answers from indexed repositories.
    MIT
  • A
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    A
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    Provides hybrid search (BM25 + vector) and staleness reporting for local markdown notes, exposing search and indexing tools to AI assistants via MCP.
    6 npm
    MIT
  • A
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    Semantic, on-demand skill retrieval for Claude Code that saves tokens and improves skill discovery by replacing the native skill listing with vector embedding search.
    8
    MIT
  • A
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    Enables SDK and MCP clients to perform repository code search and retrieval, inspect current source and related evidence, and optionally use semantic search, reranking, and web sources while keeping the calling agent in control.
    MIT
  • A
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    MCP server for searching files on macOS by name or content using semantic and lexical search, providing file paths and line numbers for agents to read from.
    MIT
  • A
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    A self-hosted, MCP-native web-search backend for AI agents that provides meta-search, clean extraction, RAG with citations, and GitHub project selection.
    5
    MIT
  • F
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    maintenance
    Enables web search and AI-summarized answers via an x402-gated gateway, with options for structured results or enriched summaries, accessible as an MCP server.
    -
  • A
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    Enables local, read-only keyword search across past Claude Code, Codex, Gemini CLI, and ChatGPT conversations plus markdown notes, with exact source citations and context retrieval.
    MIT
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    Free web search MCP server using SearXNG, supporting web search, news search, and search summaries.
    MIT
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    Enables AI agents to run semantic search over local Obsidian or Markdown vaults, returning notes ranked by meaning with paths, headings, scores, and snippets, and to trigger incremental reindexing of changed notes. Everything runs on the user's own machine, so no note content leaves it.
    MIT
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    Enables semantic search over personal Markdown journal entries by indexing them with Ollama embeddings into a SQLite vector store and exposing retrieval through a Streamable HTTP MCP endpoint at /mcp.
    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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    Enables AI clients to run local semantic search over your own documents and code in Chinese or English, with embeddings computed on an Intel NPU. Also lets clients check index status and trigger reindexing.
    MIT