Langfuse Trace Fetcher
FreeNot checkedFetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.
About
Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.
README
Version 0.1.0 · Fetch Langfuse observability traces directly into your coding agent's context.
What It Does
This is a Model Context Protocol (MCP) server that connects your VS Code coding agent (Gemini Code Assist) to a Langfuse instance. It exposes three tools:
| Tool | Description |
|---|---|
fetch_langfuse_traces |
Fetch a filtered, paginated list of traces |
get_langfuse_trace_detail |
Fetch full detail for a single trace (including observations, scores) |
list_langfuse_trace_filters |
Show available filter fields and usage examples |
Installation
From PyPI (Recommended)
pip install langfuse-traces-mcp
From Source
# Clone the repository
git clone https://github.com/yourusername/langfuse-traces-mcp.git
cd langfuse-traces-mcp
# Install in development mode (includes test dependencies)
pip install -e ".[dev]"
Prerequisites
- Python 3.10+
- VS Code with Gemini Code Assist extension (Agent Mode enabled)
- Langfuse instance — cloud (cloud.langfuse.com) or self-hosted
VS Code Setup
Install the package:
pip install langfuse-traces-mcpAdd the MCP server configuration to your VS Code settings. Open VS Code settings (Ctrl/Cmd + ,) and search for "Gemini Code Assist". In the settings JSON, add:
{
"mcpServers": {
"langfuse-traces": {
"command": "langfuse-traces-mcp"
}
}
}
- Reload VS Code after configuration.
- Open Gemini Code Assist chat and toggle Agent Mode ON.
- The
langfuse-tracestools should now be available.
Usage
Once configured, you can ask your coding agent questions like:
- "Show me traces from production in the last hour"
- "Get details for trace ID abc-123-xyz"
- "List traces with errors tagged as 'critical'"
- "Show me traces from user 'john.doe' in the staging environment"
The agent will fetch and display formatted trace data directly in the conversation.
Available Filters
| Parameter | Type | Default | Description |
|---|---|---|---|
name |
string | — | Filter by trace name |
user_id |
string | — | Filter by user ID |
session_id |
string | — | Filter by session ID |
tags |
list | — | Filter by tags |
version |
string | — | Filter by app version |
release |
string | — | Filter by release |
environment |
string | — | Filter by environment |
from_timestamp |
string | — | ISO 8601 start time |
to_timestamp |
string | — | ISO 8601 end time |
limit |
int | 20 | Max traces (1–100) |
page |
int | 1 | Page number |
Example Chat Usage
In VS Code Gemini Code Assist chat (with Agent Mode on):
Fetch the last 5 production traces from my Langfuse instance:
- Public key: pk-lf-abc123
- Secret key: sk-lf-xyz789
- Host: https://cloud.langfuse.com
- Environment: production
- Limit: 5
The agent will call fetch_langfuse_traces with those parameters and return formatted trace data.
Running Tests
# Install dev dependencies (if not already)
pip install -e ".[dev]"
# Run all tests
pytest tests/ -v
# Run a specific test file
pytest tests/test_models.py -v
pytest tests/test_client.py -v
pytest tests/test_server.py -v
Project Structure
├── pyproject.toml # Project metadata & dependencies (v0.1.0)
├── README.md # This file
├── .gemini/
│ └── settings.json # MCP server registration for VS Code
├── src/
│ └── langfuse_traces_mcp/
│ ├── __init__.py # Version export
│ ├── server.py # FastMCP server + 3 tool definitions
│ ├── client.py # Async HTTP client for Langfuse API
│ └── models.py # Pydantic models (filters, credentials)
└── tests/
├── conftest.py # Shared test fixtures & mock data
├── test_models.py # Filter & credential validation tests
├── test_client.py # REST client tests (mocked HTTP)
└── test_server.py # MCP tool integration tests
Versioning
This project follows Semantic Versioning 2.0:
- PATCH (0.1.x) — Bug fixes
- MINOR (0.x.0) — New filters, tools, or features
- MAJOR (x.0.0) — Breaking changes
License
MIT
Install Langfuse Trace Fetcher in Claude Desktop, Claude Code & Cursor
unyly install langfuse-trace-fetcherInstalls into Claude Desktop, Claude Code, Cursor & VS Code — handles npx, uvx and build-from-source repos for you.
First time? Get the CLI: curl -fsSL https://unyly.org/install | sh
Or configure manually
Run in your terminal:
claude mcp add langfuse-trace-fetcher -- uvx langfuse-traces-mcpFAQ
Is Langfuse Trace Fetcher MCP free?
Yes, Langfuse Trace Fetcher MCP is free — one-click install via Unyly at no cost.
Does Langfuse Trace Fetcher need an API key?
No, Langfuse Trace Fetcher runs without API keys or environment variables.
Is Langfuse Trace Fetcher hosted or self-hosted?
A hosted option is available: Unyly runs the server in the cloud, no local setup required.
How do I install Langfuse Trace Fetcher in Claude Desktop, Claude Code or Cursor?
Open Langfuse Trace Fetcher on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
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