Spec Driven Development
by formulahendry
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Runs on your computer★ 440MITUpdated 14 months ago
Install
Langfuse API
Overview
Description from the maker (original English)
Langfuse public API (Cloud and self-hosted): traces, prompts, datasets and gated writes.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Other
- You need to fill in: LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_BASE_URL
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- You fill in yourself:
LANGFUSE_PUBLIC_KEY,LANGFUSE_SECRET_KEY,LANGFUSE_BASE_URL,LANGFUSE_CA_CERT
Values marked secret are keys to your account. Never share them or paste them into a chat.
You need
- npmNode.js installed (for the npx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Download the maker’s .mcpb file
The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.
Run this in your terminal
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-mcpFill in the empty values before running it.
Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.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"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"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"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
Values you fill in
- The project's public key (pk-lf-...).
- The project's secret key (sk-lf-...).
- Your Langfuse host, e.g. https://cloud.langfuse.com; https only, http only for localhost.
LANGFUSE_CA_CERTenvOptional PEM file with your corporate or private CA, added to the system trust store.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account rodrigorjsf, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Otheri
- More stars than 0% of the other 32599 servers in this category
- Published to the registry more recently than 63% of the other 32599 servers in this category
- Basic checks equal to or better than 100% of the other 32599 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is Langfuse API safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
You need to fill in: LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY, LANGFUSE_BASE_URL
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.2.0) reached the registry 23 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js, Docker, MCPB on your computer.
What can I use instead?
In the same category: Spec Driven Development, Remote Desktop Commander, Zendesk
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Remote Desktop Commander
by desktop-commander
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Runs in the cloud★ 211Updated 2 days ago
Install
Zendesk
by pipeworx-io
Zendesk MCP Pack — tickets, users, organizations via OAuth.
Computer or cloudUpdated today
Install
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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 wasv0.1.0). 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 LRA[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-mcpnon-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
Only the beginning is shown; read the rest in the repo