"Chat with RocketChat" matching MCP servers:

Exposes vLLM capabilities to AI assistants, enabling chat completions, model management, and platform-aware container control with automatic detection of Docker/Podman and GPU availability across Linux, macOS, and Windows.

A minimal MCP server that exposes a run_command tool over stdio, enabling command execution within a Docker container.

Provides a universal bridge to interact with any OpenAI-compatible LLM API (local or cloud), enabling model testing, benchmarking, quality evaluation, and chat operations with performance metrics.

Enables integration of Perplexity's AI API with LLMs, delivering advanced chat completion by utilizing specialized prompt templates for tasks like technical documentation, code review, and API documentation.

Enables LLM agents to parse, validate, and solve MiniZinc constraint models directly from chat sessions, returning solutions and solver statistics.

Enables automatic weather information retrieval through a custom MCP server integrated with OpenAI using Cloudflare tunnels. Demonstrates seamless function calling between OpenAI and custom MCP tools without manual intervention.

A server enabling integration between KoboldAI's text generation capabilities and MCP-compatible applications, with features like chat completion, Stable Diffusion, and OpenAI-compatible API endpoints.

Bridges Claude Desktop with local LLM instances running via llama-server, enabling full conversation support with complete parameter control and health monitoring. Allows users to chat with their local models directly through Claude Desktop with configurable sampling parameters.

A project that integrates Model Control Protocol with OpenAI's API, allowing OpenAI to access and utilize tools exposed by a dockerized MCP server.

Gives any MCP-compatible AI chat or agent a safe, model-neutral coding runtime with file read/search, structured multi-file patches, command execution, interactive sessions, and git operations, all confined to a single workspace and gated by permission modes.

A bridge that enables Claude Code to interact with local Ollama instances for text generation, multi-turn chat, and vision-based analysis. It supports model management tasks such as listing, pulling, and showing details, alongside generating text embeddings.

A universal server that enables MCP-compatible clients (like Claude Desktop, Cursor, VS Code) to access OpenAI's APIs for chat completions, image generation, embeddings, and model listing through a standardized interface.

A template for deploying MCP servers on Vercel with serverless functions. Includes example tools for rolling dice and getting weather data to demonstrate basic functionality and API integration patterns.

An MCP server that connects AI assistants to SAP systems via the ADT REST API, enabling read, write, syntax-check, and activation of ABAP code directly from the chat.

Enables interaction with locally running Ollama models through chat, generation, and model management operations. Supports listing, downloading, and deleting models while maintaining conversation history for interactive sessions.

A server that connects Unity with local large language models through Ollama, enabling developers to automate workflows, manipulate assets, and control the Unity Editor programmatically without relying on cloud-based LLMs.

Enables AI agents to control Severe's Luau scripting environment, allowing operations like running Luau, inspecting the game tree, reading memory, and building ESP from chat.

Enables ChatGPT to securely use approved local coding and file tools, live shell sessions, durable background jobs, and external MCP integrations through a local router.

Enables LLMs to interact with and control the Pulsar text editor through a variety of file and text manipulation commands. It allows for tasks like code editing, context retrieval, and project navigation using either a built-in chat panel or external MCP clients.

A drop-in MCP server implementation for Next.js projects using Vercel MCP Adapter, allowing developers to integrate model context protocol functionality with custom tools, prompts, and resources.