"Have I Been Pwned" matching MCP servers:

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Enables agents to verify manuscript references against public scholarly records, checking that cited works exist, match their citations, and have not been retracted or flagged. It also lets an agent gather the best source passages for each citing sentence and record its own claim-support verdicts back into the report.

Enables Codex, ChatGPT, and Aside to share project work memory by reading private GitHub-backed checkpoints and searching or re-reading allowed local/SSD documents through read-only MCP tools.

Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.

Enables AI coding agents to intelligently index and search codebases with sub-20ms retrieval, 8x memory compression, and cross-encoder reranking via MCP stdio.

Enables AI agents to interact with the AI-Archive platform for research paper discovery through semantic search, paper submission and management, peer review with structured scoring, and citation generation in multiple formats.

Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.

A simple AI development tool that helps users interact with AI through natural language commands, offering 29 tools across thinking, memory, browser, code quality, planning, and time management capabilities.

Provides an MCP protocol interface for interacting with Elasticsearch 7.x databases, supporting comprehensive search functionality including aggregations, highlighting, and sorting.

Pre-trade risk validation and position sizing for AI trading agents via G-formula and Iron Fist.

缔零法则MCP是基于LLM和RAG技术搭建的实现完全替代人力的全自动化风险识别的内容安全审查平台，致力于通过代理AI技术减少人力成本，高效高精度为用户提供分钟级接入的内容风控解决方案，破解安全威胁，提供从风险感知到主动拦截策略执行的全链路闭环与一体化解决方案。This MCP tool is an AI-powered content security review platform built on LLM and RAG technologies, designed to achieve fully automated risk identification that completel

Marketplace where AI agents ask AI agents that have live or proprietary data. Anyone needing answers can ask. Anyone with the data can answer.

Provides programmatic access to UK legal documents with hybrid semantic search and Model Context Protocol integration for AI assistants.

Enables programming agents to retrieve internal R&D documentation with traceable, reproducible evidence through the MCP protocol, supporting multi-corpus routing and access control.

Enables models to query and retrieve the same documentation and data an operator would have on hand during their interactive sessions, including GitLab-hosted docs via indexed search and section lookup.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

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.

he Maximo MCP Server bridges AI agents (Claude, Cursor, Antigravity) with your IBM Maximo environment. It empowers your AI to autonomously discover Object Structures (APIs), inspect schemas, and query live data via OSLC. Instead of hallucinating field names, the AI validates queries instantly and ca

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.