Skill To Fetch Traces — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Skill To Fetch Traces (Agent Skill) and scored it 96/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 1 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 1 flagged
A bulleted imperative like {match} tells the agent to never reveal, disclose, or mention something to the user. Used adversarially it can instruct the agent to hide its tool calls or lie about what it did — stripping the transparency a user relies on to trust the agent.
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Version 0.1.0 · Fetch Langfuse observability traces directly into your coding agent's context.
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 |
pip install langfuse-traces-mcp# 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]"pip install langfuse-traces-mcp{
"mcpServers": {
"langfuse-traces": {
"command": "langfuse-traces-mcp"
}
}
}langfuse-traces tools should now be available.Once configured, you can ask your coding agent questions like:
The agent will fetch and display formatted trace data directly in the conversation.
| 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 |
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: 5The agent will call fetch_langfuse_traces with those parameters and return formatted trace data.
# 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├── 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 testsThis project follows Semantic Versioning 2.0:
MIT
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.