"Legal document templates and contract drafting resources" matching MCP servers:

Enables semantic search over Turkish legal contracts and emails via an MCP server integrated with Claude Desktop and Claude Code, providing tools for document ingestion, hybrid vector search, and full-text retrieval.

Provides AI agents with multi-format document indexing, hybrid dense and sparse search with reranking, and relational SQL querying over extracted tables.

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

Enables real-time indexing and semantic search of local documents (PDF, Word, text, Markdown, RTF) using vector embeddings and local LLMs. Monitors folders for changes and provides natural language search capabilities through Claude Desktop integration.

MCP server enabling Claude Desktop to answer questions from local Word and PDF documents by searching a vector index built from their contents.

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

A deterministic MCP server for document processing and retrieval that enables AI agents to process, chunk, and vector-search documents without an LLM.

Enables AI agents to search and retrieve relevant document content from existing embeddings stored in Supabase vector database. Provides semantic search capabilities to find document chunks based on similarity to query text without generating new embeddings.

Enables semantic search and question-answering over uploaded documents using vector embeddings and Google AI. Supports document organization with tags, section-aware queries, and hierarchical markdown structure preservation.

MCP server that indexes uploaded PDF, DOCX, and TXT documents into an isolated per-session in-memory vector index and retrieves the exact matching passages behind each answer. It exposes document indexing and search tools to a Q&A backend so every response is grounded in cited, retrieved evidence.

This MCP server provides semantic document search and retrieval, enabling AI assistants to search documents, search categories, and retrieve category hierarchies using the Model Context Protocol.

An MCP server that enables Claude Desktop or Claude Code to ingest and search local documents using vector embeddings, returning relevant chunks as tool results for the model to reason over.

Transforms CSV and Excel data into Markdown-formatted vector embeddings stored in a local ChromaDB instance for semantic search. It enables MCP clients to retrieve relevant tabular data through single-row, batch, or free-text queries.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

Enables AI systems to remember interactions, understand document context through semantic search, and intelligently route requests with persistent memory and quality-scored content synthesis.

A legal-tech focused system that coordinates specialized agents for document classification, deadline extraction from Spanish legal texts, and strategic business intelligence. It integrates with Claude via MCP to provide semantic document search and automated deadline tracking using a Supabase vector database.

将 pgvector 知识库检索能力封装为标准 MCP 服务器，支持 Tools、Resources 和 Prompts，使 Claude Desktop 等客户端可通过自然语言查询知识库。

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Enables AI agents to parse PDFs and search local document context through multimodal extraction and vector-based retrieval, all exposed as MCP tools.

Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.