"Information about AliDocs (Alibaba's document collaboration platform)" matching MCP servers:

Provides AI agents with comprehensive document parsing capabilities including PDF text extraction, OCR, HTML-to-markdown conversion, table extraction, and summarization, optimized for agent workflows.

Enables collaboration with multiple AI providers (Claude, GPT-4, Gemini, Ollama) directly from VS Code with automatic project context injection and persistent conversation history. Provides streamlined tools for getting AI advice, multi-provider research, and enhanced context sharing across sessions.

Indexes local documents (PDF, Word, Markdown, text) into a SQLite database for AI agents to search and retrieve bounded, source-located passages. Runs fully locally with optional OCR, preserving privacy.

A local document evidence layer for MCP clients that ingests documents (PDF, Office, images) with optional OCR, indexes them in SQLite FTS, and provides retrieval tools with source coordinates.

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

Enables AI agents to search, deep-read, and build knowledge bases from Markdown, PDF, DOCX, and PPTX documents via MCP tools for retrieval, document navigation, and ingestion.

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.

Enables NPC agents to read, write, search, and manage a shared corporate document store, and to turn Mission Scripts into structured report outlines and scaffolds aligned to strategic intent.

An MCP server that uses the Docling toolkit to convert various document formats, including PDFs, Office files, images, and audio, into clean Markdown for AI processing. It supports multiple processing pipelines like VLM and ASR with intelligent auto-detection and job queue management.

Enables reading and searching documents in allowlisted folders via LiteLLM and MCP tools, using COM for Office files to support IRM-protected content.

Efficiently delivers project documentation to AI agents like Claude on-demand, optimizing token usage by loading context only when needed. Supports document retrieval, listing, and keyword search with security features.

Enables MCP-based access to Adhoc documentation, providing search, content retrieval, and feedback issue creation with audience gating and leak protection.

A Model Context Protocol server that provides document analysis capabilities to LLM applications, including extraction, chunking, summarization, and semantic search for PDF, DOCX, and plaintext documents.

Enables local semantic search over PDF, DOCX, PPTX, and EPUB documents by converting them to Markdown, indexing them in ChromaDB, and retrieving complete structure-aware sections with tables and equations.

MCP server for integrating manufacturing systems (MES/ERP/quality/maintenance) with LLM agents, enabling event ingestion, incident triage, approval workflows, and RAG-based knowledge retrieval.

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 AI assistants to directly access quant research knowledge, including factor libraries, strategy backtesting, and research reports, through the MCP protocol.

Enables MCP hosts like Cursor or Claude to query an enterprise knowledge base via stdio, providing RAG-based answers with cited sources.

A local MCP server that answers natural language questions over a collection of research PDFs, using semantic retrieval and Gemini for grounded answers with citations.

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