# com.pulsemcp/langfuse

> Use this tool when you need to analyze and observe Large Language Models (LLMs) for tracing and observation analysis. It solves problems related to LLM observability, providing insights into model performance and behavior. The tool accepts LLM data as input and outputs detailed analysis and tracing information, ideal for use cases where model transparency and understanding are crucial.

Canonical page: https://skillsregistry.net/skills/com-pulsemcp-langfuse  
JSON: https://api.skillsregistry.net/v1/skills/com-pulsemcp-langfuse

## Description

MCP server for Langfuse LLM observability — trace and observation analysis.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** unverified

## Facts

- **Version:** 0.1.2
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-02

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/com.pulsemcp%2Flangfuse)
- **Repository:** <https://github.com/pulsemcp/mcp-servers>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "com-pulsemcp-langfuse"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/com-pulsemcp-langfuse` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/com-pulsemcp-langfuse/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt