"A tool for document extraction using MinerU" matching MCP servers:

Enables document parsing and extraction from PDFs and other formats using the MinerU API. Supports batch processing, page range selection, OCR in 109 languages, and VLM/pipeline models for high-accuracy content extraction.

Extract invoices and contracts from text or Markdown into typed JSON with Mistral. Optional OCR supports PDFs and images when your account has access and quota. Six tools by default: documents, OCR, chat, vision, code completion and transcription. Additional API tools via explicit profiles. Runs over stdio or Streamable HTTP. Community-maintained; bring your own Mistral API key.

MCP router that fronts your other MCP servers behind a few small generic tools, so their tool definitions stay out of the context window and are discovered on demand.

A local document processing toolkit for AI agents that extracts text, converts PDFs to Markdown, merges files, extracts tables, and summarizes documents without external API dependencies.

Audits MCP tool descriptions for quality and reliability, scoring them 0-100, detecting smells, and providing rewritten versions for better agent accuracy.

Graph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.

Enables agents to extract multi-page financial tables from complex PDFs, rasterize vector pages into crisp 300 DPI images for vision LLMs, flatten interactive AcroForm/XFA forms, and dewarp skewed OCR bounding boxes from photographed or scanned paper documents. Tool invocations are gated by USDC micropayments settled on Base L2 via the x402 protocol.

Legal Document AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

Protects AI agents from bad tool outputs, schema mismatches, and tool poisoning by validating, sanitizing, and scoring reliability of tool responses.

Document Comparison AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

Cross-chain cryptocurrency swaps via Chainflip. Get quotes, execute swaps, and track progress. No API key required to get started.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

Use Codex as a subagent inside Claude Code: async dispatch into a long-lived codex app-server, results pushed back into the Claude session automatically, steer/interrupt mid-task, and Codex's built-in code review.

Odds are, most of what you want to build is already open source. Tell your AI agent what you're making, and it finds it: which repos to use as they are, which to adapt, and what's left for you to write. Every repo it names is real, licensed for your use or flagged, and checked live.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

Enables redaction and sanitization of document PII and credentials through MCP, including Luhn credit-card verification, reversible salt hashing, and audit logging.

MCP server that detects and guards against tool poisoning and prompt injection attacks in tool descriptions and schemas. It provides risk scoring, pattern detection, safe rewriting, and audit reports with zero external API cost.

Lets a human review what an AI agent is about to post to a chat tool (Slack first) before it is sent. Renders the draft, destination and warnings (@here/@channel, unresolved mentions, format issues) in a neutral local HTML preview; never sends anything.

Analyzes multi-step AI agent tool chains to compute success probability, identify bottlenecks, and suggest better execution orders, enabling more reliable agents via local pure-math computation.