"Understanding Unrestricted Model Abilities" matching MCP servers:

An MCP server that retrieves and provides AI model information from OpenAI, Anthropic, and Google through a unified interface.

An MCP server that turns the Hugging Face Hub into a conversational model picker with a tool-use eval harness to prove the agent actually uses it correctly.

ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a conceptual fork of OrientDB, the first Multi-Model DBMS. ArcadeDB supports Vector Embeddings.

An MCP server that queries multiple Ollama models and combines their responses, providing diverse AI perspectives on a single question for more comprehensive answers.

Provides a standardized protocol for tool invocation, enabling an AI system to search the web, retrieve information, and provide relevant answers through integration with LangChain, RAG, and Ollama.

A demonstration server for the Model Context Protocol (MCP) that exposes calculator and Yahoo Finance tools, allowing LLMs to interpret natural language requests and make tool calls via the MCP standard.

A unified API server that enables interaction with multiple AI model providers like Anthropic and OpenAI through a consistent interface, supporting chat completions, tool calling, and context handling.

A middleware server that intelligently routes AI model queries to appropriate data sources, providing contextual information to enhance AI responses.

A conversational application server that integrates LLM capabilities via Ollama with vector memory context, supporting multiple users, sessions, automatic history summarization, and a plugin system for executing real actions.

A server exposing intelligent tools for enhancing RAG applications with entity extraction, query refinement, and relevance checking capabilities.

An MCP server that enables AI models to retrieve information from Ragie's knowledge base through a simple 'retrieve' tool.

IMCP - Insecure Model Context Protocol The DVWA for AI Security! Welcome to IMCP – a deliberately vulnerable framework that exposes 16 critical security weaknesses in AI/ML systems. Whether you're a security researcher, developer, or educator, IMCP is your playground for hands-on learning about real

Access to many public datasets right from your LLM application.

A Model Context Protocol server that enables LLMs to interact directly with MongoDB databases, allowing users to query collections, inspect schemas, and manage data through natural language.

Enables AI systems to securely query databases using structured intents (MDBP) with automatic schema discovery and policy enforcement.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

A secure multi-tenant server that provides real-time context to LLMs via REST API, using JWT authentication and PostgreSQL row-level security for tenant isolation.

A comprehensive production-ready MCP server with AI integration, plugin management, and web-based administration. Features multi-database support, RAG capabilities, SSH/SFTP access, and a built-in plugin hub for managing the MCP ecosystem.

Provides brain-inspired long-term memory for MCP clients, enabling persistent user understanding across sessions with Chinese-optimized hybrid retrieval and evolving user profiles.

Cline MCP integration that allows users to save, search, and format memories with semantic understanding, providing tools to store and retrieve information using vector embeddings for meaning-based search.