"Extracting Orders from PDF Files" matching MCP connectors:

Manage your Mistral platform — models, files, batch jobs, agents and RAG document libraries.

Extract PDFs to Markdown, RAG chunks and cited tables; publish tracked Doc Links with read stats.

Collaborative, cache-first web search for agents — cited answers from a shared live-web pool.

Parse PDF/Word/PPT/HTML to Markdown; tables as JSON, image extraction, RAG chunking, page ranges.

Generate 18 AI readiness files (llms.txt, ai.txt, RAG indexes, schema) for any website.

A personal RAG database you build from chat, so AI creates work that sounds like you.

Unstructured document processing for LLM pipelines. Upload as PDF/DOCX/TXT any supported files, extract structured data (PII-redacted), build LLM-ready datasets, and search/export results — all via MCP tools (document.process, job.status, job.result, dataset.build, dataset.search, dataset.export).

MCP server for German & EU law. Verified, citable legal context for any LLM. Daily updates from official sources, hosted in Germany

CareerProof MCP gives AI agents direct access to a professional-grade career and workforce intelligence platform. Two namespaces: atlas_* for HR/TA teams (candidate evaluation, batch shortlisting, competency scoring, interview generation, JD analysis, custom eval frameworks, research reports) and ceevee_* for professionals (CV optimization, career positioning, salary intelligence, market reports). Backed by RAG knowledge from 50+ premium research sources (McKinsey, BCG, HBR, Gartner, WEF)

Persistent memory for AI assistants. Save once; recall from Claude, ChatGPT, or any MCP client.

Cross-vendor AI memory over MCP. One semantic store, readable and writeable from every MCP client.

Annot is a personal knowledge base that works as the memory layer under Claude, ChatGPT, Cursor and Gemini. Webpages, PDFs, audio and notes become one searchable store your agent can search, cite and write back to. Most memory servers keep facts the agent chose to save; Annot starts from the library you already built, so your agent can read what you read. 29 tools: search, context loading, write-back, topics, export. Annot account required (invite-only beta).

Vector RAG store for Word/Excel/PDF/PowerPoint. Break-even pricing, $5 per 5,700 pages.

Team knowledge from 20 connectors, served to any MCP agent — classified, scored, access-controlled.

Direct access to 40+ scraping and search tools. Extract structured data from Google (Search, Maps, Trends), Amazon, Airbnb, Social Media, and any web page directly into your AI agent.

Search ~8.5M products from 2,500+ Central European e-shops. Semantic, keyword, GTIN lookup.

Search your knowledge bases from any AI assistant using hybrid RAG.

LLMtoMD is the memory layer for AI coding agents. It converts any document — PDF, DOCX, slides, spreadsheets, images, audio, even whole websites — into clean, structured Markdown, then exposes it over MCP so your agent can search your FRDs, specs, and API docs on demand instead of re-reading (or forgetting) them.

Reliable PDF table extraction. Pass a URL, get structured JSON tables with citations.

MCP-native knowledge base for AI agents — vault-scoped docs, tables, and files, git-versioned, with hybrid search (BM25 + pgvector dense + reranker) and an event stream so external consolidators / gardeners stay decoupled.