# Langfuse MCP Server

<!-- mcp-name: io.github.avivsinai/langfuse-mcp -->

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**Usage:** [12,518 PyPI downloads last month](https://pypistats.org/packages/langfuse-mcp) ([pypistats](https://pypistats.org/api/packages/langfuse-mcp/recent), 2026-08-19). [v0.10.1](https://pypi.org/project/langfuse-mcp/0.10.1/).

Local [MCP](https://modelcontextprotocol.io) server and skill for [Langfuse](https://langfuse.com). Debug traces, sessions, and exceptions from Claude Code, Codex, Cursor, or any MCP client.

## Why this instead of native Langfuse MCP?

Use this for local debug: first-class traces, sessions, and exceptions; route-decision tools; compact / file-dump output; plus the included [`langfuse` skill](skills/langfuse/SKILL.md).

Use official [Langfuse MCP](https://langfuse.com/docs/api-and-data-platform/features/mcp-server) for hosted, zero-install access to the broader API (score writes, comments, models, media).

As of June 2026:

| | langfuse-mcp | Native Langfuse MCP |
|-|--------------|---------------------|
| **Primary fit** | Local debug: traces, sessions, exceptions | Hosted, zero-install API surface |
| **Deployment** | Local `stdio` or HTTP, via the Langfuse Python SDK | Native streamable HTTP at `/api/public/mcp` |
| **Trace / session / exception tools** | First-class | Observation/API-oriented access |
| **Route-decision tools** | Yes | No |
| **Token & output control** | Compact summaries, truncation, file-dump mode, tool-group gating | Hosted tool response + client |
| **Metrics & dataset runs** | Yes | Yes |
| **Prompt, dataset, queue & score reads** | Yes | Yes |
| **Score writes, comments, models, media** | No | Yes |

`langfuse-mcp` for local debug. Native MCP for hosted breadth.

## See a failing trace in 2 minutes

Install is `uvx langfuse-mcp` plus Langfuse API keys — [Quick Start](#quick-start) for Claude Code, Codex, Cursor, or Docker.

After the client restarts, ask:

```text
find exceptions in the last day
```

That maps to existing tools:

```text
find_exceptions(age=1440, group_by="file")
find_exceptions_in_file(filepath="<file from the grouping>", age=1440)
get_exception_details(trace_id="<trace_id from the file results>")
```

`find_exceptions` returns `{group, count, observation_id, trace_id}` (top 50 groups) — each group carries a representative observation/trace ID, and `find_exceptions_in_file` also yields full error records. Then optionally:

```text
fetch_trace(trace_id="<trace_id>", include_observations=true)
```

The exception tools detect errors by observation `level == "ERROR"` (strict; a non-empty `status_message` alone does not count). Counts describe error-level observations, not individual exception events — `get_error_count` returns `exception_count: null` for that reason. Exception details come from recorded metadata (`exception.type` / `exception.message` / `exception.stacktrace`, top-level or under `metadata.attributes`); absent values are null. Results are not a point-in-time snapshot.

## Project Links

- [Quick Start](#quick-start)
- [Agent Skill](#agent-skill)
- [Tools](#tools-48-total)
- [Selective Tool Loading](#selective-tool-loading)
- [Read-Only Mode](#read-only-mode)
- [Other Clients](#other-clients)

## Quick Start

Requires [uv](https://docs.astral.sh/uv/getting-started/installation/) (for `uvx`) and Python 3.10 or newer. CI verifies Python 3.10 through 3.14.

Get credentials from [Langfuse Cloud](https://cloud.langfuse.com) → Settings → API Keys. If self-hosted, use your instance URL for `LANGFUSE_HOST`.

```bash
# 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 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 langfuse-mcp
```

To pin a CI-verified interpreter explicitly, add `--python 3.14` before `langfuse-mcp`.

Restart your CLI, then verify with `/mcp` (Claude Code) or `codex mcp list` (Codex).

## Agent Skill

This repo ships a first-party [`langfuse` skill](skills/langfuse/SKILL.md) for Claude Code and Codex. The skill gives agents concrete playbooks for trace debugging, exception triage, latency analysis, prompt management, and dataset work.

Install it when you want the agent to know when to reach for Langfuse and which MCP tools to call first.

**Via [skills](https://github.com/vercel-labs/add-skill)** (recommended):
```bash
npx skills add avivsinai/langfuse-mcp -g -y
```

**Via [skild](https://skild.sh)**:
```bash
npx skild install @avivsinai/langfuse -t claude -y
```

**Manual install:**
```bash
cp -r skills/langfuse ~/.claude/skills/   # Claude Code
cp -r skills/langfuse ~/.codex/skills/    # Codex CLI
```

After installing the skill, try:

```text
help me debug langfuse traces
find exceptions in the last day
why was this user's session slow?
```

The MCP server provides the tools; the skill provides the agent-facing workflow. See [`skills/langfuse/SKILL.md`](skills/langfuse/SKILL.md), [`skills/langfuse/references/setup.md`](skills/langfuse/references/setup.md), and [`skills/langfuse/references/tool-reference.md`](skills/langfuse/references/tool-reference.md).

## Tools (48 total)

| Category | Tools |
|----------|-------|
| Traces | `fetch_traces`, `fetch_trace` |
| Observations | `fetch_observations`, `fetch_observation` |
| Routing | `find_route_decisions`, `get_route_decision`, `summarize_route_decisions`, `find_low_confidence_route_decisions` |
| 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`, `list_dataset_runs`, `get_dataset_run`, `list_dataset_run_items`, `create_dataset_run_item`, `delete_dataset_run` |
| 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` |
| Metrics | `query_metrics`, `get_metrics_schema` |
| 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.

```python
create_dataset_item(dataset_name="qa-test-cases", item_id="item_123", input={"question": "What is 2+2?"}, expected_output={"answer": "4"})
```

## Metrics Queries

`query_metrics` aggregates telemetry server-side (cost, latency, tokens, counts, score values) so agents can answer "what did inference cost?" or "what's p95 latency by model?" without pulling raw traces. Call `get_metrics_schema` for the full view/dimension/measure catalog.

```python
query_metrics(
    view="observations",
    metrics=[{"measure": "totalCost", "aggregation": "sum"}, {"measure": "latency", "aggregation": "p95"}],
    dimensions=["providedModelName"],
    age=1440,  # last 24h; or pass from_timestamp / to_timestamp
)
```

High-cardinality fields (`id`, `traceId`, `userId`, `sessionId`) must be used in `filters`, not `dimensions`. The v2 metrics endpoint is Langfuse Cloud-only; self-hosted instances may return 404.

## Selective Tool Loading

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

```bash
langfuse-mcp --tools traces,prompts
```

Available groups: `traces`, `observations`, `routing`, `sessions`, `exceptions`, `prompts`, `datasets`, `annotation_queues`, `scores`, `metrics`, `schema`

The `routing` group is router-neutral. It reads Langfuse span observations with
`metadata.schema_version: "mcp.route_decision.v1"` and filters on route-decision
fields stored in observation metadata, such as `decision_id`, `router_name`,
`provider`, and `capability_id`.

## Read-Only Mode

Disable all write operations for safer read-only access:

```bash
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`, `create_dataset_run_item`, `delete_dataset_run`, `create_annotation_queue`, `create_annotation_queue_item`, `update_annotation_queue_item`, `delete_annotation_queue_item`, `create_annotation_queue_assignment`, `delete_annotation_queue_assignment`

## Default Output Mode

Set the MCP-exposed default `output_mode` so clients that omit the parameter automatically use your preferred mode:

```bash
langfuse-mcp --default-output-mode full_json_file
# Or via environment variable
LANGFUSE_MCP_DEFAULT_OUTPUT_MODE=full_json_file langfuse-mcp
```

Supported values: `compact`, `full_json_string`, `full_json_file`

This updates the default shown in MCP tool schemas. Clients can still override it per call by passing `output_mode` explicitly.

## Other Clients

### Cursor

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

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

### Docker (single project)

```bash
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
```

### HTTP transport — shared server for multiple projects

Run one persistent server instance and route each MCP client to its own Langfuse
project by passing credentials in the `Authorization` header.

```bash
# Start a shared server (binds to localhost by default)
docker run -d -p 127.0.0.1:8000:8000 \
  -e LANGFUSE_HOST=https://cloud.langfuse.com \
  ghcr.io/avivsinai/langfuse-mcp:latest \
  --transport streamable-http --bind-host 0.0.0.0
```

> **Security note:** `--bind-host 0.0.0.0` exposes the port on all interfaces. In
> production, place the server behind a TLS-terminating reverse proxy (nginx, Caddy,
> Cloudflare Tunnel) that enforces HTTPS. The `Authorization` header containing your
> keys is transmitted in plaintext over plain HTTP. If startup credentials are set,
> the proxy must enforce authentication; otherwise unauthenticated callers without an
> `Authorization` header can use the default project. For shared public HTTP deployments,
> omit default `LANGFUSE_PUBLIC_KEY` / `LANGFUSE_SECRET_KEY` credentials unless the
> fronting proxy authenticates every request.

Register each project separately in your MCP client, passing its credentials as a
`Basic` auth header (`base64(public_key:secret_key)`):

```bash
# Generate the header value for each project:
echo -n "pk-lf-YOURKEY:sk-lf-YOURSECRET" | base64
# cGstbGYtWU9VUktFWTpzay1sZi1ZT1VSU0VDUkVU

# Register in Claude Code (one entry per project):
claude mcp add langfuse-audit \
  --transport http http://localhost:8000/mcp \
  -H "Authorization: Basic cGstbGYtWU9VUktFWTpzay1sZi1ZT1VSU0VDUkVU"

claude mcp add langfuse-staging \
  --transport http http://localhost:8000/mcp \
  -H "Authorization: Basic <base64 for staging project>"
```

**Auth semantics:** `Basic` here carries Langfuse API keys, not user passwords. An
absent header falls back to startup env credentials (`LANGFUSE_PUBLIC_KEY` /
`LANGFUSE_SECRET_KEY`). Any malformed header is rejected outright — there is no
silent fallback to a different project.

### Optional environment variables

| Variable | Default | Description |
|---|---|---|
| `LANGFUSE_MAX_AGE_DAYS` | `7` | Caps the lookback window for time-based tools (`fetch_traces`, `fetch_observations`, etc.). Set to match your Langfuse instance's data retention — e.g. `30` if your retention is 30 days. |
| `LANGFUSE_MCP_TRACE_TIMEOUT_SECONDS` | `120` | Per-request read timeout (seconds) for single-trace fetches (`fetch_trace`). Raise it if large traces with `include_observations=True` time out. Must be a positive integer. |

## Development

```bash
uv venv --python 3.14 .venv && source .venv/bin/activate
uv pip install -e ".[dev]"
pytest
```

## License

MIT