A Model Context Protocol (MCP) server for Langfuse, enabling AI agents to query Langfuse trace data for enhanced debugging and observability
{
"mcpServers": {
"langfuse-mcp": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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A Model Context Protocol (MCP) server for Langfuse, enabling AI agents to query Langfuse trace data for enhanced debugging and observability
Is it safe?
No package registry to scan.
No authentication — any process on your machine can connect.
MIT. View license →
Is it maintained?
Last commit 4 days ago. 73 stars.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
Context cost
37 tools. ~1,500 tokens (0.8% of 200K). Consider loading selectively.
No automated test available for this server. Check the GitHub README for setup instructions.
No known vulnerabilities.
fetch_tracesFetch traces from Langfuse
fetch_traceFetch a specific trace from Langfuse
fetch_observationsFetch observations from Langfuse
fetch_observationFetch a specific observation from Langfuse
fetch_sessionsFetch sessions from Langfuse
get_session_detailsGet details for a specific session
get_user_sessionsGet sessions for a specific user
find_exceptionsFind exceptions in Langfuse
find_exceptions_in_fileFind exceptions in a specific file
get_exception_detailsGet details for a specific exception
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Model Context Protocol server for Langfuse observability. Query traces, debug errors, analyze sessions, manage prompts.
Comparison with official Langfuse MCP (as of Jan 2026):
| | langfuse-mcp | Official | |-|--------------|----------| | Traces & Observations | Yes | No | | Sessions & Users | Yes | No | | Exception Tracking | Yes | No | | Prompt Management | Yes | Yes | | Dataset Management | Yes | No | | Annotation Queues | Yes | No | | Scores (v2) | Yes | No | | Selective Tool Loading | Yes | No |
This project provides a full observability toolkit — traces, observations, sessions, exceptions, prompts, datasets, annotation queues, and scores — while the official MCP focuses on prompt management.
Requires uv (for uvx).
Get credentials from Langfuse Cloud → Settings → API Keys. If self-hosted, use your instance URL for LANGFUSE_HOST.
# Claude Code (project-scoped, shared via .mcp.json)
claude mcp add \
-e LANGFUSE_PUBLIC_KEY=pk-... \
-e LANGFUSE_SECRET_KEY=sk-... \
-e LANGFUSE_HOST=https://cloud.langfuse.com \
--scope project \
langfuse -- uvx --python 3.11 langfuse-mcp
# Codex CLI (user-scoped, stored in ~/.codex/config.toml)
codex mcp add langfuse \
--env LANGFUSE_PUBLIC_KEY=pk-... \
--env LANGFUSE_SECRET_KEY=sk-... \
--env LANGFUSE_HOST=https://cloud.langfuse.com \
-- uvx --python 3.11 langfuse-mcp
Restart your CLI, then verify with /mcp (Claude Code) or codex mcp list (Codex).
| Category | Tools |
|----------|-------|
| Traces | fetch_traces, fetch_trace |
| Observations | fetch_observations, fetch_observation |
| Sessions | fetch_sessions, get_session_details, get_user_sessions |
| Exceptions | find_exceptions, find_exceptions_in_file, get_exception_details, get_error_count |
| Prompts | list_prompts, get_prompt, get_prompt_unresolved, create_text_prompt, create_chat_prompt, update_prompt_labels |
| Datasets | list_datasets, get_dataset, list_dataset_items, get_dataset_item, create_dataset, create_dataset_item, delete_dataset_item |
| Annotation Queues | list_annotation_queues, create_annotation_queue, get_annotation_queue, list_annotation_queue_items, get_annotation_queue_item, create_annotation_queue_item, update_annotation_queue_item, delete_annotation_queue_item, create_annotation_queue_assignment, delete_annotation_queue_assignment |
| Scores | list_scores_v2, get_score_v2 |
| Schema | get_data_schema |
Langfuse uses upsert for dataset items. To edit an existing item, call create_dataset_item with item_id. If the ID exists, it updates; otherwise it creates a new item.
create_dataset_item(
dataset_name="qa-test-cases",
item_id="item_123",
input={"question": "What is 2+2?"},
expected_output={"answer": "4"}
)
This project includes a skill with debugging playbooks.
Via skills (recommended):
npx skills add avivsinai/langfuse-mcp -g -y
Via skild:
npx skild install @avivsinai/langfuse -t claude -y
Manual install:
cp -r skills/langfuse ~/.claude/skills/ # Claude Code
cp -r skills/langfuse ~/.codex/skills/ # Codex CLI
Try asking: "help me debug langfuse traces"
See [skills/langfuse/SKILL.md](skil