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    A multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.
    3
    1
    MIT
  • A
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    Enables RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.
    6
    MIT
  • A
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    maintenance
    Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
    4
    AGPL 3.0
  • F
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    Searches local markdown files using TF-IDF retrieval and serves full documents as resources.
    1
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  • A
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    A local MCP server that enables LLMs to securely search and read files within designated Google Drive folders. Supports Google Docs, Google Sheets, PDFs, and plain text with strict folder-scoping via service account authentication.
    4
    MIT
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    Provides tools to test regex patterns for correctness, performance (ReDoS), and memory usage, and suggests safe rewrites. Enables LLMs to iterate on regex generation with verifiable feedback.
    9
    MIT
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    MCP-сервер налоговых/бухгалтерских калькуляторов ФНС + публичные фискальные статусы (самозанятый, ИП/ОКВЭД, дисквалификация, блокировки счетов). Волна 1, юр-риск минимальный.
    12
    44 PyPI
    1
    MIT
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    Provides read/write file tools with lint validation to prevent agents from writing malformed Obsidian markdown, and constrains writes to a single configurable vault for security.
    7
    MIT
  • A
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    Local-first RAG MCP server that indexes local documents and provides hybrid search (semantic + keyword) with file ingestion, URL fetching, and a CLI, keeping data on-device.
    11
    67
    MIT