Open-source tool for collaborative editing, versioning, evaluating, and releasing prompts.
If you want an AI assistant working directly with Langfuse Prompt Management, the langfuse prompt management mcp server is the bridge. Open-source tool for collaborative editing, versioning, evaluating, and releasing prompts.
Model Context Protocol (MCP) Server for Langfuse Prompt Management. This server allows you to access and manage your Langfuse prompts through the Model Context Protocol.
Once connected, the assistant can call these 4 tools directly:
get-prompts — List available promptsget-prompt — Retrieve and compile a specific promptTools — To increase compatibility with other MCP clients that do not support the prompt capability, the server also exports tools that replicate the functionality ofCursor — The Cursor tool exposed by this serverThe server is distributed via npm as @modelcontextprotocol/inspector, so most clients can run it without a manual build step. Add it to your MCP client's configuration and restart the client to pick it up — the copy-paste configs for Claude Desktop, Claude Code and Cursor are on this page.
Before the server will start you need to supply 3 environment variables: LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_BASEURL. 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. Langfuse Prompt Management sits in that group, and the shape of its toolset — get-prompts, get-prompt, Tools among others — 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 |
|---|---|
| get-prompts | List available prompts |
| get-prompt | Retrieve and compile a specific prompt |
| Tools | To increase compatibility with other MCP clients that do not support the prompt capability, the server also exports tools that replicate the functionality of the MCP Prompts. |
| Cursor | The Cursor tool exposed by this server. |
{
"mcpServers": {
"langfuse": {
"command": "node",
"args": ["<absolute-path>/build/index.js"],
"env": {
"LANGFUSE_PUBLIC_KEY": "your-public-key",
"LANGFUSE_SECRET_KEY": "your-secret-key",
"LANGFUSE_BASEURL": "https://cloud.langfuse.com"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| LANGFUSE_PUBLIC_KEY | Credential the server authenticates with. | Yes |
| LANGFUSE_SECRET_KEY | Credential the server authenticates with. | Yes |
| LANGFUSE_BASEURL | Endpoint or connection string the server talks to. | 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.