"A service for document recognition, assessment, and filling in evaluations" matching MCP servers:

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

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.

OpenAI-compatible vision recognition MCP server that lets MCP clients see images by routing them to any OpenAI-compatible vision model.

Persistent memory and pattern learning for AI coding assistants via MCP.

Analyzes images, audio, and video using Google's Gemini AI, with support for downloading videos from URLs such as Bilibili and YouTube.

Enables querying documents through a Langflow backend using natural language questions, providing an interface to interact with Langflow document Q\&A flows.

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.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

A production-ready generic FastMCP server template with SQLAlchemy async CRUD, enabling rapid bootstrapping of MCP data-management services. It provides registry-driven CRUD, FK resolution, conflict versioning, and multiple transports.

Enables intelligent document processing by extracting text, classifying document types, and generating structured summaries from PDFs and images using vision LLMs.

A secure Model Context Protocol server that enables Claude AI to list, search, and read local documents in formats such as PDF, DOCX, and XLSX. It features read-only access with path traversal protection and supports deployment via Docker with SSE or STDIO transport options.

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.

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.

Provides image understanding via Volcano Ark's doubao-seed-2.1-turbo multimodal model, offering tools to describe images and extract text (OCR) from image URLs or local paths.

A production-minded RAG service for MCP that answers questions over your documents with hybrid retrieval, PII redaction, and source citations, packaged for Docker/Kubernetes.

A comprehensive document analysis server that performs sentiment analysis, keyword extraction, readability scoring, and text statistics while providing document management capabilities including storage, search, and organization.

Checks documents against international standards (e.g., ICAO, Hague-Visby) to verify internal consistency and completeness, returning a machine-readable verdict for agent decision-making.

Connects AI assistants to Jira Service Management instances, providing 40 tools for managing service desks, requests, comments, customers, organizations, queues, SLA, approvals, and attachments.