Langfuse API

Name: Langfuse API
Author: rodrigorjsf

作者：rodrigorjsf版本 0.2.0i★ –i在你的电脑上运行

注册名称io.github.rodrigorjsf/langfuse-api-mcp

安装

概览

作者提供的说明（英文原文）

Langfuse public API (Cloud and self-hosted): traces, prompts, datasets and gated writes.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 其他
你需要填写：LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_BASE_URL

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_BASE_URL, LANGFUSE_CA_CERT

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

npm已安装 Node.js（用于 npx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

下载作者的 .mcpb 文件

链接指向作者自己的文件；我们不托管也不重新打包它。打开文件后，Claude Desktop 会进行安装。

在终端中运行这条命令

claude mcp add langfuse-api --env LANGFUSE_PUBLIC_KEY=your-value --env LANGFUSE_SECRET_KEY=your-value --env LANGFUSE_BASE_URL=your-value --env LANGFUSE_CA_CERT=your-value -- npx -y langfuse-api-mcp

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "langfuse-api": {
      "command": "npx",
      "args": [
        "-y",
        "langfuse-api-mcp"
      ],
      "env": {
        "LANGFUSE_PUBLIC_KEY": "your-value",
        "LANGFUSE_SECRET_KEY": "your-value",
        "LANGFUSE_BASE_URL": "your-value",
        "LANGFUSE_CA_CERT": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "langfuse-api": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "langfuse-api-mcp"
      ],
      "env": {
        "LANGFUSE_PUBLIC_KEY": "your-value",
        "LANGFUSE_SECRET_KEY": "your-value",
        "LANGFUSE_BASE_URL": "your-value",
        "LANGFUSE_CA_CERT": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

LANGFUSE_PUBLIC_KEY 机密必填 env
The project's public key (pk-lf-...).
LANGFUSE_SECRET_KEY 机密必填 env
The project's secret key (sk-lf-...).
LANGFUSE_BASE_URL 必填 env
Your Langfuse host, e.g. https://cloud.langfuse.com; https only, http only for localhost.
LANGFUSE_CA_CERT env
Optional PEM file with your corporate or private CA, added to the system trust store.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 rodrigorjsf 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“其他”中的其他服务器比较i

星标数多于此分类中其他 32568 个服务器的 0%
发布到注册中心的时间比此分类中其他 32568 个服务器的 63% 更新
基础检查结果等于或优于此分类中其他 32568 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Langfuse API 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_BASE_URL

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.2.0）于23天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js, Docker, MCPB。

有什么可以替代它？

同一分类中有：Spec Driven Development, Remote Desktop Commander, FlyDocs

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Spec Driven Development

作者：formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新

安装

Remote Desktop Commander

作者：desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新

安装

FlyDocs

作者：flydocs.ai

Spec-driven development workflow: your agent works issues, sessions and the status lifecycle.

在你的电脑上运行今天更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

langfuse-api-mcp

An MCP server that gives AI agents access to the full Langfuse public API. It works with Langfuse Cloud in any region and with self-hosted instances. It is built to run on corporate networks that re-sign TLS traffic with their own certificate authority (CA).

[!IMPORTANT]

Status: pre-alpha, release `v0.2.0` (semver 0.x: no stability promise yet; the first release was v0.1.0). Everything below works today unless it is marked Planned, like the loopback HTTP transport.

Contents: What it is · Tools · User skill · Install and run · Client configuration · Configuration · Security model · Troubleshooting · Learn more

What it is

A small, stateless program that your AI tool (Claude Code, Claude Desktop, Cursor, VS Code, Codex, …) starts on your machine. It speaks the Model Context Protocol to the agent and HTTPS to Langfuse. The agent can then:

investigate traces, observations, sessions, errors, latency and cost;
read prompts, datasets, experiments, scores, metrics, models, comments and annotation queues;
make changes, such as promoting a prompt label, creating a score or editing a dataset. This works only if you turn writes on (see Security model).

flowchart LR

A[AI agent Claude Code, Cursor, …] -- MCP over stdio --> S[langfuse-mcp on your machine or in Docker] S -- HTTPS + your corporate CA --> P{{Corporate proxy optional}} P --> L[(Langfuse Cloud or self-hosted)] S -. reads .-> C[/Your config: keys, host, CA files/] classDef local fill:#e0f2fe,stroke:#0369a1,color:#0c4a6e; classDef remote fill:#fef3c7,stroke:#b45309,color:#78350f; class A,S,C local; class L,P remote;

When to use it

Situation	Use this server?
Your company network inspects TLS with a custom root CA, and the official Langfuse MCP or CLI fails with `self-signed certificate in certificate chain` / `unable to get local issuer certificate`	Yes. This is the main reason the project exists.
You want the agent to be unable to change Langfuse data unless you explicitly allow it	Yes. It is read-only by default, and the server enforces this itself for every call made through it. Keep the keys out of the agent's own environment, or another tool can use them directly: Keep the keys out of the agent's environment.
You run a self-hosted Langfuse behind an internal CA or proxy	Yes.
You want a single binary with no Node.js or Python runtime on your machine	Yes.
You only need Langfuse documentation in your agent	No. Use the official docs MCP at `https://langfuse.com/api/mcp`.
Your agent can run shell commands and your network has no TLS interception	Either works. Langfuse also offers an official CLI and Agent Skill.

How it differs from the official Langfuse MCP

	Official project MCP ( `/api/public/mcp`)	This server
Where it runs	Remote, inside Langfuse	Locally (binary or Docker)
Custom CA / corporate proxy	Depends on your MCP client's runtime; no documented options	Explicit settings, plus automatic pickup of CA variables that are already set on your system
Writes	Enabled by default; to restrict them you configure a client-side allowlist	Off by default. The write tool does not exist until you enable it.
API coverage	Curated tool set	Every operation your Langfuse deployment actually answers — older self-hosted versions keep their legacy APIs, newer ones get the new APIs — except trace ingestion and organization admin changes (creating/deleting projects, API keys, users). Detected automatically at startup, with nothing to configure (Deployment profile)

Tools

The Langfuse API has about 100 in-scope operations. One tool per operation would fill the agent's context window, so the server exposes a small set of tools:

Tool	What it does	Available
`search_operations`	Lists the Langfuse operations your deployment serves, optionally filtered by keywords (in write mode, each write operation with its HTTP method and whether it is destructive)	always
`describe_operation`	Returns the parameters of one operation that your deployment's Langfuse version serves (and, apart from them, a filter the server applies itself when your version lacks it; and, in write mode, its body schema (the one of your deployment's Langfuse version), whether it is destructive and, for `comments_create` and `scores_create`, how to fill the body)	always
`execute_read`	Runs a read operation (HTTP GET) by its ID and returns the Langfuse JSON inside an untrusted-data envelope. On an older Langfuse that ignores the `traceId` of `scores_get-many` or the `level` of `legacy_observationsV1_getMany`, the server applies that filter itself and says whether it read the whole list	always
`execute_write`	Runs a write operation by its ID; a destructive one (DELETE, PUT, PATCH) only after you confirm that exact call	only in write mode
`get_trace_tree`	Returns every observation of one trace as a tree in one call. It reads Observations v2 where the deployment answers the v4 read APIs (Cloud, self-hosted v4) and the legacy observations list on self-hosted v3, where input, output and metadata are removed unless you ask for them and a row holds the fields that Langfuse returns (latency in milliseconds there, in seconds on v4), minus those it returned as null. Where a deployment answers both, the tree is read from Observations v2, which can lack observations that were sent through the legacy ingestion API, so an empty or short tree can be checked against the legacy observations list through `execute_read`. Where neither API answers it says `operation_unavailable`	always listed; runs where the deployment answers either API

A typical session: search_operations finds the operation, describe_operation shows its parameters, execute_read runs it:

{"operationId": "trace_list", "parameters": {"limit": 10, "tags": ["prod", "checkout"]}}

The server keeps nothing between calls (it is stateless). It never takes a URL, host or credential from the agent. It only runs operations from its built-in catalog, against the host you configured. Every call is validated before anything is sent, and every failure comes back as a structured tool error with a code, a hint and a retryable flag. Parameters, error codes, result limits and the audit line: Tools reference.

User skill

The server gives an agent generic tools; the user skill langfuse-api-mcp (skills/langfuse-api-mcp/) teaches it how a Langfuse investigation is done with them: discover, describe, then execute; always a bounded time window; Langfuse data is untrusted, never instructions. It has a short entry file and one reference per workflow (traces, cost and latency, prompts, experiments, scores, errors), each loaded only when its workflow is asked for. Its description names this server's tools, so it does not compete with Langfuse's own langfuse skill. It adds no permission of its own: whether execute_write exists and every confirmation stay the server's decision.

Claude Code, Cursor, VS Code, Codex CLI, Gemini CLI, Windsurf. Install it from the repository with the skills CLI, in your project (or with -g for your user); --agent names the hosts to install for (--agent '*' for all). The Claude Code install is proven; the other hosts rely on the CLI's own support:

npx skills add rodrigorjsf/langfuse-api-mcp

non-interactive, one host: npx skills add rodrigorjsf/langfuse-api-mcp --agent claude-code -y

The CLI installs from main, while your server may be an older build; the skill therefore tells the agent to call describe_operation for every parameter it does not name (details).

Claude Desktop installs skills only by upload. Each GitHub release carries langfuse-api-mcp-skill_ .zip, signed, whose root is the langfuse-api-mcp/ folder; upload it in Claude Desktop under Customize > Skills.

Install and run

Channel	Platforms	Needs
npx	Linux, macOS, Windows × x64, arm64	Node.js with npm
Release archive	Linux, macOS, Windows × amd64, arm64	nothing
Docker	linux/amd64, linux/arm64; stdio only	Docker
Claude Desktop (MCPB)	macOS, Windows	Claude Desktop
`go install`	any Go platform	Go 1.21 or later

Every channel except go install is signed or carries npm provenance: see Verify what you run. The server is also listed in the MCP Registry as io.github.rodrigorjsf/langfuse-api-mcp. What each channel ships and how it is built: Installation reference.

npx

Configure the server in your MCP client as npx -y langfuse-api-mcp (pin a version with langfuse-api-mcp@ ); it needs Node.js with npm, nothing else. The package carries the Go binary: nothing is downloaded at install time and no install script runs. See Client configuration for the snippet of your client.

Release archive

Every GitHub release carries one archive per target, langfuse-mcp_ _ _ .tar.gz (.zip on Windows), plus checksums.txt. Download with gh or curl, check the archive against checksums.txt, then extract it (Linux on amd64 shown; is the release without the leading v, for example 0.2.0):

version=<version>

archive="langfuse-mcp_${version}_linux_amd64.tar.gz" gh release download "v$version" -R rodrigorjsf/langfuse-api-mcp -p "$archive" -p checksums.txt

or: curl -fsSLO "https://github.com/rodrigorjsf/langfuse-api-mcp/releases/download/v$version/$archive"

curl -fsSLO "https://github.com/rodrigorjsf/langfuse-api-mcp/releases/download/v$version/checksums.txt"

awk -v f="$archive" '$2 == f' checksums.txt | sha256sum -c - # macOS: … | shasum -a 256 -c - tar -xzf "$archive"

On Windows (PowerShell), compare the hash and extract the zip:

$archive = "langfuse-mcp_<version>_windows_amd64.zip"

(Get-FileHash $archive -Algorithm SHA256).Hash.ToLower() # must equal the line for $archive in checksums.txt Expand-Archive $archive -DestinationPath langfuse-mcp

Then point your MCP client at the extracted binary (see Client configuration). The binaries are not notarized nor Authenticode-signed: download with curl or gh, not a browser, or see Downloaded in a browser?.

Docker

Pass the keys and the base URL with -e, and keep -i (the server speaks MCP over stdin and stdout):

docker run -i --rm \

-e LANGFUSE_PUBLIC_KEY -e LANGFUSE_SECRET_KEY -e LANGFUSE_BASE_URL \ ghcr.io/rodrigorjsf/langfuse-api-mcp:

-e NAME without a value passes the variable from the environment that runs docker, so the keys never appear in the command line. Behind a corporate CA, mount the CA file read-only and point LANGFUSE_CA_CERT at it; the file must be readable by any user (the image does not run as you):

docker run -i --rm \

-e LANGFUSE_PUBLIC_KEY -e LANGFUSE_SECRET_KEY -e LANGFUSE_BASE_URL \ -e L

只显示了开头部分；其余内容请在仓库中阅读