A Ruby gem that exposes Large Language Models (LLMs) via the Model Context Protocol (MCP), enabling seamless integration of AI capabilities into your
Llm mcp mcp server lets Claude, Cursor and other MCP clients work with Llm MCP directly. A Ruby gem that exposes Large Language Models (LLMs) via the Model Context Protocol (MCP), enabling seamless integration of AI capabilities into your development workflow.
A Ruby gem that exposes Large Language Models (LLMs) via the Model Context Protocol (MCP), enabling seamless integration of AI capabilities into your development workflow.
llm-mcp creates an MCP server that provides standardized access to various LLM providers (OpenAI, Google Gemini, and OpenAI-compatible APIs) while supporting advanced features like session management, conversation persistence, and integration with external MCP tools.
Once connected, the assistant can call these 2 tools directly:
task — The task tool exposed by this serverreset_session — The reset_session tool exposed by this serverSetup follows the standard MCP pattern: clone or install the server, then register it in your client's configuration file and restart the client. The configuration snippets on this page cover Claude Desktop, Claude Code and Cursor.
Before the server will start you need to supply 4 environment variables: GITHUB_TOKEN, OPENAI_API_KEY, GEMINI_API_KEY, GOOGLE_API_KEY. Keep credentials in your client's env block or a secrets manager rather than committing them.
AI-service servers chain other models into your assistant, turning a single chat into a small production pipeline. Llm MCP sits in that group, and the shape of its toolset — task, reset_session — tells you what it is really for. Worth comparing against the other ai services servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
| Tool | What it does |
|---|---|
| task | The task tool exposed by this server. |
| reset_session | The reset_session tool exposed by this server. |
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": ["@modelcontextprotocol/server-filesystem", "/tmp"]
},
"github": {
"command": "mcp-github",
"env": {
"GITHUB_TOKEN": "your-github-token"
}
},
"http-api": {
"url": "https://api.example.com/mcp/sse",
"transport": "sse",
"headers": {
"Authorization": "Bearer your-token"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| GITHUB_TOKEN | Credential the server authenticates with. | Yes |
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| GEMINI_API_KEY | Credential the server authenticates with. | Yes |
| GOOGLE_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.