langfuse-mcp by avivsinai - MCP Server

Langfuse MCP Server

Model Context Protocol server for Langfuse observability. Query traces, debug errors, analyze sessions, manage prompts.

Why langfuse-mcp?

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
Selective Tool Loading	Yes	No

This project provides a full observability toolkit — traces, observations, sessions, exceptions, and prompts — while the official MCP focuses on prompt management.

Quick Start

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).

Tools (25 total)

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`
Schema	`get_data_schema`

Dataset Item Updates (Upsert)

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"}
)

Skill

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 for full documentation.

Selective Tool Loading

Load only the tool groups you need to reduce token overhead:

langfuse-mcp --tools traces,prompts

Available groups: traces, observations, sessions, exceptions, prompts, datasets, schema

Read-Only Mode

Disable all write operations for safer read-only access:

langfuse-mcp --read-only
# Or via environment variable
LANGFUSE_MCP_READ_ONLY=true langfuse-mcp

This disables: create_text_prompt, create_chat_prompt, update_prompt_labels, create_dataset, create_dataset_item, delete_dataset_item

Other Clients

Cursor

Create .cursor/mcp.json in your project (or ~/.cursor/mcp.json for global):

{
  "mcpServers": {
    "langfuse": {
      "command": "uvx",
      "args": ["--python", "3.11", "langfuse-mcp"],
      "env": {
        "LANGFUSE_PUBLIC_KEY": "pk-...",
        "LANGFUSE_SECRET_KEY": "sk-...",
        "LANGFUSE_HOST": "https://cloud.langfuse.com"
      }
    }
  }
}

Docker

docker run --rm -i \
  -e LANGFUSE_PUBLIC_KEY=pk-... \
  -e LANGFUSE_SECRET_KEY=sk-... \
  -e LANGFUSE_HOST=https://cloud.langfuse.com \
  ghcr.io/avivsinai/langfuse-mcp:latest

Development

uv venv --python 3.11 .venv && source .venv/bin/activate
uv pip install -e ".[dev]"
pytest

License

MIT