A Rust-based gRPC server that provides a standardized interface to Core Lightning nodes. This server implements the MCP (Model Context Protocol)
CLN MCP MCP server exists for a simple reason — assistants are far more useful when they can act on CLN MCP directly instead of describing what you should do. A Rust-based gRPC server that provides a standardized interface to Core Lightning nodes. This server implements the MCP (Model Context Protocol) specification to enable control of the Core Lightning node using LLM.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client.
Once CLN MCP is connected, these are the calls the assistant has available:
ca.pem — CA certificateclient.pem — Client certificateclient-key.pem — Client private keyAmong the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. CLN MCP's toolset — ca.pem, client.pem, client-key.pem — is a fair guide to whether it matches your workflow. It is maintained by adi2011; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
| Tool | What it does |
|---|---|
| ca.pem | CA certificate |
| client.pem | Client certificate |
| client-key.pem | Client private key |
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.