"A system for legal document analysis and contract management" matching MCP servers:

Quick-scan a smart contract for rug, honeypot, or centralization risk before sending funds. It combines verified source, live on-chain state, and heuristic Solidity analysis to return a SAFE/CAUTION/HIGH-RISK verdict.

Enables LLMs to ingest and analyze legal agreements, compute risk scores, and monitor non-compliant clauses through MCP tools like ingest, fetch_contracts, and run_analysis.

MCP server for Korean public procurement law, offering LLM-free deterministic rule-based contract-method decisions, statute/regulation search, and live court precedents from law.go.kr.

Analyzes financial contract PDFs to extract clauses, flag risk terms, and compare contract versions, producing structured risk briefs for legal and risk teams.

An MCP server for Indian legal work that enables case-law research with verified citations, limitation and deadline calculations, legal document drafting, matter tracking, and contract search.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

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.

Governance framework for LLM agents. Exposes seven mechanically-enforced rules plus tools for preflight file protection, audit-chain verification, session handoff and worklog templates, MCP config auditing, and an emergency kill switch.

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

Enables AI assistants to access options analysis data, market research tools, portfolio risk snapshots, and platform context via 32 tools.

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.

MCP server for alive-analysis. Query, search, and retrieve structured analysis history written with the ALIVE loop (Ask→Look→Investigate→Voice→Evolve), plus dashboard JSON export.

Provides specialized tools for portfolio health analysis, rebalance simulations, and trade execution via a single interface powered by MongoDB. It enables AI agents to manage investment portfolios while adhering to organizational governing rules, clearance guards, and rate limits.

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

A production-ready template for developing Model Context Protocol (MCP) servers using Python and FastMCP.

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.

A comprehensive Model Context Protocol (MCP) server for product inventory management with PostgreSQL database backend, enabling natural language queries for product information across multiple AI platforms.