"Reading Microsoft OneDrive files" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
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).
Generate 18 AI readiness files (llms.txt, ai.txt, RAG indexes, schema) for any website.
Manage your Mistral platform — models, files, batch jobs, agents and RAG document libraries.
Serve a folder of Markdown notes as an MCP server: hybrid search, reading, and sourced answers.
One semantic search across your sites, Drive, Notion, email, files and Basecamp.
Interact with The Drive AI files directly from AI assistants like ChatGPT or Claude using the Model Context Protocol (MCP).
Search one ClearMakes agent's knowledge: your documents, website pages and Drive files.
Rafter holds a team's durable knowledge — skills, agents and memory files, each versioned — and serves it to AI tools over MCP. Agents search across the team's artifacts before answering questions about how the team works or what was decided, fetch full artifact text along with its citation edges (cites, cited_by, links) to explore related material, and write new learnings back as memory. Also covers workspace, team and membership management.
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.
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.
The Needle MCP server enables semantic search on documents stored in files like PDFs, DOCX, and XLSX by connecting AI applications to external data sources. It provides capabilities to create and manage document collections, perform natural language searches on stored content, and retrieve relevant information without requiring exact keyword matches.
