Langfuse Docs MCP lets AI agents search and access Langfuse documentation, enabling coding agents to find integration guidance, API references, and implementation examples for Langfuse.
Cursor is an AI-first code editor. It loads MCP servers from its settings and lets its agent call their tools as part of a task.
This guide connects Langfuse Docs MCP to Cursor through MewCP, so Cursor can use Langfuse Docs MCP's 4 tools, like submitFeedback, searchLangfuseDocs and getLangfuseDocsPage.
Langfuse Docs MCP needs no credentials, but every call still runs through MewCP: scoped, checked and logged like any other.
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Langfuse Docs MCP works the same way in every client below. Pick yours for its own guide.
Free to start.
Scope access to the exact Langfuse Docs MCP tools and accounts you allow, work and personal side by side. Anything outside that scope is refused at call time, even when the model asks for it.
Each call is logged: what ran, on which account, and whether it worked. Failures come back structured, so the agent knows whether to retry instead of guessing.
Every app sits behind one secure MCP URL, and there is no server for you to deploy or keep running. Add an integration in the dashboard and it is live on the next call, with no config to change and no new credentials to wire up. Tools load only when a task needs them, so your agent stays fast and focused however many apps you connect.
A browser window opens the first time the client connects. Sign in and approve access. No API key needed.
Start a new chat and ask: "List the tools you can use from MewCP." You should see your connected apps.
Submit explicit user-approved feedback to the Langfuse team. Before calling, ask permission and show the exact payload. If the user wants a reply, ask them to include their email address in the feedback text; only use an address they explicitly provide and show it in the exact payload preview. Do not include secrets, credentials, customer/project data, trace payloads, or unrelated context; the only contact detail to include is an explicitly provided reply email.
Semantic search (RAG) over the Langfuse documentation. Use this whenever the user asks a broader question that cannot be answered by a specific single page. Returns a concise answer synthesized from relevant docs. The raw provider response is included in _meta. Prefer this before guessing. If a specific page is needed call getLangfuseDocsPage first.
Fetch the raw Markdown for a single Langfuse docs page. Accepts a docs path (e.g., /docs/observability/overview) or a full https://langfuse.com URL. Returns the exact Markdown (may include front matter). Use when you need a specific page content (Integration, Features, API, etc.) or code samples. Prefer searchLangfuseDocs for broader questions where there is not one specific page about it. Changelog pages (/changelog/...) are historical release notes: use them only to confirm that a feature exists or when it shipped, not as an implementation reference. Their code samples reflect the SDK/API at release time and may be outdated, so for implementation follow the current docs and the API/SDK reference instead.
Get a high-level, machine-readable index by downloading https://langfuse.com/llms.txt. Use this at the start of a session when needed to discover key docs endpoints or to seed follow-up calls to searchLangfuseDocs or getLangfuseDocsPage. Returns the plain text contents of llms.txt. Avoid repeated calls within the same session.
