log-logn/langfuse-mcp-java
FreeNot checkedQuery Langfuse traces and observations, debug exceptions, analyze sessions, scores and datasets, and manage prompts.
About
Query Langfuse traces and observations, debug exceptions, analyze sessions, scores and datasets, and manage prompts.
README
A production-grade MCP server that connects any MCP-compatible AI agent to your Langfuse observability data.
Query traces, debug errors, inspect sessions, manage prompts, run evaluations, annotate data, and configure models — all through natural language.
Transport: Streamable HTTP on port 8080, compatible with Cursor, Claude Desktop, VS Code / GitHub Copilot, and any MCP client that supports HTTP transport.
Why this server?
| Capability | This server | Official Langfuse MCP |
|---|---|---|
| Traces & Observations | ✅ | ❌ |
| Sessions & Users | ✅ | ❌ |
| Exception tracking | ✅ | ❌ |
| Prompt management (read + write) | ✅ | ✅ read-only |
| Dataset & run management | ✅ | ❌ |
| Scores & score configs | ✅ | ❌ |
| Annotation queues | ✅ | ❌ |
| Comments | ✅ | ❌ |
| Model definitions | ✅ | ❌ |
| LLM connections | ✅ | ❌ |
| Project introspection | ✅ | ❌ |
| Schema introspection | ✅ | ❌ |
| Java / Spring AI | ✅ | ❌ (Python) |
Prerequisites
- Java 21 or later
- Maven 3.9+ (or use the Docker build — no local Maven required)
- A Langfuse account with an API key pair (
public-key+secret-key)
Quick Start
# 1. Build
mvn clean package -DskipTests
# 2. Set credentials
export LANGFUSE_PUBLIC_KEY=pk-lf-...
export LANGFUSE_SECRET_KEY=sk-lf-...
export LANGFUSE_HOST=https://cloud.langfuse.com
# 3. Run (Streamable HTTP transport — port 8080)
java -jar target/langfuse-mcp-1.0.0.jar
# 4. Verify
curl http://localhost:8080/actuator/health
# 5. Inspect all tools
npx @modelcontextprotocol/inspector http://localhost:8080/mcp
Get credentials from Langfuse Cloud → Settings → API Keys.
Self-hosted Langfuse? Set LANGFUSE_HOST to your instance URL.
Configuration
All configuration is driven by environment variables (or application.yml for local overrides).
| Property | Env var | Required | Default | Description |
|---|---|---|---|---|
langfuse.public-key |
LANGFUSE_PUBLIC_KEY |
✅ | — | Langfuse project public key |
langfuse.secret-key |
LANGFUSE_SECRET_KEY |
✅ | — | Langfuse project secret key |
langfuse.host |
LANGFUSE_HOST |
✅ | — | Langfuse base URL, e.g. https://cloud.langfuse.com |
langfuse.timeout |
LANGFUSE_TIMEOUT |
❌ | 30s |
HTTP request timeout — Spring Duration format, e.g. 30s, 1m, 90s |
langfuse.read-only |
— | ❌ | true |
Informational flag; write operations are available through specific tools |
Trailing slash handling
LANGFUSE_HOST may be specified with or without a trailing slash — the server normalises it automatically.
Client Configuration
Cursor (.cursor/mcp.json)
{
"mcpServers": {
"langfuse": {
"url": "http://localhost:8080/mcp"
}
}
}
Claude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"langfuse": {
"url": "http://localhost:8080/mcp"
}
}
}
On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
VS Code / GitHub Copilot
URL mode:
{
"github.copilot.chat.mcp.servers": {
"langfuse": {
"url": "http://localhost:8080/mcp"
}
}
}
Command mode (stdio-only clients):
{
"github.copilot.chat.mcp.servers": {
"langfuse": {
"command": "java",
"args": ["-jar", "/absolute/path/to/langfuse-mcp-1.0.0.jar"],
"env": {
"LANGFUSE_PUBLIC_KEY": "pk-lf-...",
"LANGFUSE_SECRET_KEY": "sk-lf-...",
"LANGFUSE_HOST": "https://cloud.langfuse.com"
}
}
}
}
Note: The MCP endpoint is
/mcp(streamable HTTP). The legacy SSE/sseendpoint is not used by this server.
Docker
The Dockerfile is a multi-stage build: it compiles the Spring Boot jar inside Docker and runs the MCP server on port 8080. No local Maven installation is needed.
# Build image (compiles inside Docker)
docker build -t langfuse-mcp:latest .
# Run
docker run --rm -p 8080:8080 \
-e LANGFUSE_PUBLIC_KEY=pk-lf-... \
-e LANGFUSE_SECRET_KEY=sk-lf-... \
-e LANGFUSE_HOST=https://cloud.langfuse.com \
langfuse-mcp:latest
After the container starts:
| Endpoint | URL |
|---|---|
| Health check | http://localhost:8080/actuator/health |
| Ping | http://localhost:8080/ping |
| MCP endpoint | http://localhost:8080/mcp |
Langfuse running in another container on the same host:
-e LANGFUSE_HOST=http://host.docker.internal:3000
Tools Reference (55 total)
Every tool returns a consistent ApiResponse<T> envelope:
{ "success": true, "data": { ... }, "timestamp": "2025-01-15T10:30:00Z" }
{ "success": false, "errorCode": "TRACE_NOT_FOUND", "errorMessage": "...", "timestamp": "..." }
Paginated list responses wrap their items in a PagedResponse<T>:
{
"data": [ ... ],
"meta": { "page": 1, "limit": 20, "totalItems": 142, "totalPages": 8 }
}
Pagination is 1-based (page defaults to 1). limit defaults to 20 and is capped at 100 where noted. To page through results, increment page while keeping limit fixed.
Traces (8 tools)
| Tool | Description |
|---|---|
fetch_traces |
Paginated list of traces. Filter by userId, name, sessionId, tags, fromTimestamp, toTimestamp. |
fetch_trace |
Full detail of a single trace including nested observations, input/output, metadata, latency, and token usage. Requires traceId. |
find_exceptions |
Traces whose level equals ERROR. Supports time range and pagination. |
find_exceptions_in_file |
Error-level traces whose metadata contains a given file name substring. Requires fileName. |
get_exception_details |
Full detail of a single error trace. Requires traceId. |
get_error_count |
Count of ERROR-level traces in a time range (scans up to 500 traces). |
delete_trace |
Permanently deletes a single trace by ID. Irreversible. |
delete_traces |
Permanently deletes multiple traces. Pass a comma-separated list of trace IDs. Irreversible. |
Sessions (3 tools)
| Tool | Description |
|---|---|
fetch_sessions |
Paginated list of sessions with optional time range filter. |
get_session_details |
Full session detail including all its traces. Requires sessionId. |
get_user_sessions |
All sessions for a specific user with pagination. Requires userId. |
Prompts (5 tools)
| Tool | Description |
|---|---|
list_prompts |
Paginated list of all prompts in the project. |
get_prompt |
Fetch a prompt by name. Optionally pin to a version number or a label (e.g. production, staging). |
create_prompt |
Create a new prompt or append a new version to an existing prompt. type is text (plain string) or chat (JSON array of {role, content} messages). Supports comma-separated labels and tags. |
delete_prompt |
Delete prompt versions by name. Scope to a specific label or version; omit both to delete all versions. Irreversible. |
update_prompt_labels |
Replace the full label set on a specific prompt version. Supply an empty string to remove all labels. The latest label is reserved by Langfuse. |
Datasets (7 tools)
| Tool | Description |
|---|---|
list_datasets |
Paginated list of all evaluation datasets. |
get_dataset |
Fetch a dataset by exact name. |
create_dataset |
Create a new dataset. Optionally supply description, metadataJson, inputSchemaJson, and expectedOutputSchemaJson (all as JSON strings). |
list_dataset_items |
Paginated list of items in a dataset. Requires datasetName. |
get_dataset_item |
Fetch a single dataset item by ID. |
create_dataset_item |
Create or upsert a dataset item. Optionally link to a sourceTraceId or sourceObservationId. Supports itemId for upsert semantics. |
delete_dataset_item |
Permanently delete a dataset item by ID. Irreversible. |
Dataset Runs (5 tools)
| Tool | Description |
|---|---|
list_dataset_runs |
Paginated list of experiment runs for a dataset. Requires datasetName. |
get_dataset_run |
Full run detail including all run items. Requires datasetName and runName. |
delete_dataset_run |
Delete a run and all its items. Irreversible. Requires datasetName and runName. |
list_dataset_run_items |
Paginated list of items in a run. Requires datasetId and runName. |
create_dataset_run_item |
Create a run item linking a dataset item to a trace/observation. Creates the run automatically if it does not yet exist. |
Metrics (1 tool)
| Tool | Description |
|---|---|
get_cost_metrics |
Query Langfuse cost, token, latency, and usage analytics via the Metrics API v1. Mirrors: GET /api/public/metrics?query= |
This tool accepts a single required parameter query which must be a JSON-serialised string matching the Metrics API schema. Examples (pass these as a single JSON string):
Total cost last 7 days:
{"view":"traces","metrics":[{"measure":"totalCost","aggregation":"sum"}],"fromTimestamp":"2026-03-18T00:00:00Z","toTimestamp":"2026-03-25T23:59:59Z"}
Daily cost trend this week:
{"view":"traces","metrics":[{"measure":"totalCost","aggregation":"sum"},{"measure":"count","aggregation":"count"}],"timeDimension":{"granularity":"day"},"fromTimestamp":"2026-03-18T00:00:00Z","toTimestamp":"2026-03-25T23:59:59Z"}
Cost by model:
{"view":"observations","dimensions":[{"field":"providedModelName"}],"metrics":[{"measure":"totalCost","aggregation":"sum"},{"measure":"totalTokens","aggregation":"sum"}],"fromTimestamp":"2026-03-18T00:00:00Z","toTimestamp":"2026-03-25T23:59:59Z"}
Cost for a specific user:
{"view":"traces","metrics":[{"measure":"totalCost","aggregation":"sum"}],"filters":[{"column":"userId","operator":"=","value":"user-123","type":"string"}],"fromTimestamp":"2026-03-18T00:00:00Z","toTimestamp":"2026-03-25T23:59:59Z"}
Production environment only:
filters: [{"column":"environment","operator":"=","value":"production","type":"string"}]
Scores (6 tools)
| Tool | Description |
|---|---|
get_scores |
Paginated list of evaluation scores. Filter by traceId, observationId, name, dataType (NUMERIC|CATEGORICAL|BOOLEAN), and time range. |
get_score |
Fetch a single score by ID. |
get_score_configs |
Paginated list of score config schemas. |
get_score_config |
Fetch a single score config by ID. |
create_score_config |
Create a score config. NUMERIC supports optional minValue/maxValue. CATEGORICAL accepts a categoriesJson array of {label, value} objects. |
update_score_config |
Update an existing score config. Optionally set isArchived to archive it. |
Annotation Queues (8 tools)
| Tool | Description |
|---|---|
list_annotation_queues |
Paginated list of annotation queues. |
get_annotation_queue |
Fetch a single queue by ID. |
create_annotation_queue |
Create a queue for human-in-the-loop review. Optionally link a scoreConfigId. |
list_annotation_queue_items |
Paginated list of items in a queue. Optionally filter by status (PENDING|COMPLETED). Requires queueId. |
get_annotation_queue_item |
Fetch a specific queue item by queueId and itemId. |
create_annotation_queue_item |
Add a trace, observation, or session to a queue for review. objectType is TRACE, OBSERVATION, or SESSION. |
update_annotation_queue_item |
Update the status of a queue item (PENDING|COMPLETED). |
delete_annotation_queue_item |
Remove an item from a queue. Irreversible. |
Comments (3 tools)
| Tool | Description |
|---|---|
get_comments |
Paginated list of comments. Optionally filter by objectType (TRACE|OBSERVATION) and objectId. |
get_comment |
Fetch a single comment by ID. |
create_comment |
Attach a comment to a trace, observation, session, or prompt. objectType values: TRACE, OBSERVATION, SESSION, PROMPT. |
Models (4 tools)
| Tool | Description |
|---|---|
list_models |
Paginated list of all model definitions (Langfuse-managed and custom). |
get_model |
Fetch a model definition by ID. |
create_model |
Create a custom model for cost tracking. Requires modelName, matchPattern (regex), and unit (TOKENS|CHARACTERS|MILLISECONDS|SECONDS|IMAGES|REQUESTS). Optionally set per-unit USD prices. |
delete_model |
Delete a custom model definition. Langfuse-managed models cannot be deleted. Irreversible. |
LLM Connections (2 tools)
| Tool | Description |
|---|---|
list_llm_connections |
Paginated list of LLM provider connections (secret keys are masked in the response). |
upsert_llm_connection |
Create or update a provider connection by provider name (e.g. openai, anthropic, azure, google). Upserts by provider — if a connection already exists it is updated. |
Project (1 tool)
| Tool | Description |
|---|---|
get_projects_for_api_key |
Returns the project(s) visible to the configured API key. Useful for confirming credentials and project metadata. |
Users (1 tool)
| Tool | Description |
|---|---|
get_user_traces |
All traces for a specific Langfuse user ID with pagination. Requires userId. |
Schema (1 tool)
| Tool | Description |
|---|---|
get_data_schema |
Returns the full Langfuse data model: all entity types, fields, and valid enum values. Call this first to understand the available data structures before running queries. |
Architecture
MCP Client (Cursor / Claude Desktop / Copilot / other)
│ Streamable HTTP transport (/mcp)
▼
Tool class (@McpTool — validates required params, delegates to service)
▼
Service interface + impl (business logic, filtering, error mapping)
▼
LangfuseApiClient (HTTP gateway — GET / POST / PATCH / DELETE, typed exceptions)
▼
Langfuse Public REST API
The architecture is strictly layered:
client/— Langfuse integration boundary: HTTP with Basic-Auth (Apache HttpComponents 5), typed exceptions,UriComponentsBuilderfor query paramsservice/— domain logic: filtering, mapping, pagination, error translation intoApiResponsetools/— MCP surface: agent-friendly descriptions, parameter validation, delegation to services- Spring Boot — runtime and transport wrapper only
API Client
LangfuseApiClient supports four HTTP methods. All methods throw LangfuseApiException or ResourceNotFoundException on error, which the service layer converts into structured ApiResponse.error(...) responses — agents never see raw stack traces.
| Method | Used for |
|---|---|
GET |
All read operations |
POST |
Create operations |
PATCH |
Update operations |
DELETE |
Delete operations |
Package Structure
com.langfuse.mcp
├── LangfuseMcpApplication.java @SpringBootApplication @ConfigurationPropertiesScan
├── config/
│ ├── LangfuseProperties.java @ConfigurationProperties — publicKey, secretKey, host, timeout, readOnly
│ ├── LangfuseClientConfig.java RestClient bean — Basic-Auth, Apache HttpComponents 5, configurable timeout
│ └── JacksonConfig.java Primary ObjectMapper (JSR310, ignore unknown fields)
├── client/
│ └── LangfuseApiClient.java HTTP gateway (GET/POST/PATCH/DELETE); typed exceptions; UriComponentsBuilder queries
├── controller/
│ └── PingController.java GET /ping → {"status":"ok"}
├── exception/
│ ├── LangfuseApiException.java Wraps HTTP/connectivity errors — statusCode + endpoint
│ └── ResourceNotFoundException.java Thrown on HTTP 404
├── dto/
│ ├── common/ ApiResponse · PagedResponse · PaginationMeta
│ ├── request/ Filter/get request classes (12 classes)
│ └── response/ Response classes (19 classes — JsonNode for open-schema fields)
├── service/ Interfaces (15): Trace · Session · Prompt · PromptWrite · Dataset · DatasetRun
│ │ · Score · AnnotationQueue · Comment · Model · LlmConnection · Project · User · Schema · CostMetrics
│ └── impl/ *ServiceImpl (15) — business logic, filtering, error mapping
├── tools/ @McpTool classes (15) — param validation, delegation, agent-friendly descriptions
│ ├── TraceTools.java (8 tools)
│ ├── SessionTools.java (3 tools)
│ ├── PromptTools.java (2 tools)
│ ├── PromptWriteTools.java (3 tools)
│ ├── DatasetTools.java (7 tools)
│ ├── DatasetRunTools.java (5 tools)
│ ├── ScoreTools.java (6 tools)
│ ├── AnnotationQueueTools.java (8 tools)
│ ├── CommentTools.java (3 tools)
│ ├── ModelTools.java (4 tools)
│ ├── LlmConnectionTools.java (2 tools)
│ ├── ProjectTools.java (1 tool)
│ ├── UserTools.java (1 tool)
│ ├── SchemaTools.java (1 tool)
│ └── CostMetricsTools.java (1 tool)
└── util/
└── JsonPageMapper.java Centralised JSON → PagedResponse mapper (no duplication)
Running Tests
mvn test
Test coverage includes:
LangfusePropertiesBindingTest— config binding fromapplication-test.ymland property-level validationPromptWriteServiceImplTest— service logic for prompt create / delete / label updateProjectServiceImplTest— project API response mappingObservationServiceImplTest— observation fetch and field mappingMetricsServiceImplTest— metrics aggregation logic
Tests run with spring.ai.mcp.server.enabled=false (set in src/test/resources/application-test.yml) so no MCP transport is started during test execution.
Troubleshooting
TRACE_FETCH_ERROR: HTTP/1.1 header parser received no bytes
Connectivity issue — not a code bug. Check:
LANGFUSE_HOSTpoints to a running Langfuse instance- The host is reachable from the JVM process
- For Docker: use
host.docker.internalinstead oflocalhost - The scheme matches your server (
http://vshttps://) - Confirm the API is up:
curl $LANGFUSE_HOST/api/public/health
INVALID_INPUT: <param> is required
A required parameter was not provided. All required = true parameters are validated at the tool layer before any HTTP call is made.
Connection timeouts
Increase the timeout:
export LANGFUSE_TIMEOUT=60s
Agent cannot see the server
- Confirm the server is running:
curl http://localhost:8080/actuator/health - Confirm the MCP endpoint is reachable:
curl http://localhost:8080/ping - Check that the client config URL points to
http://localhost:8080/mcp - Inspect all available tools:
npx @modelcontextprotocol/inspector http://localhost:8080/mcp
Langfuse-managed models cannot be deleted
delete_model only works for custom model definitions you have created. To override a Langfuse-managed model's pricing, create a new custom model with the same modelName.
License
Installing log-logn/langfuse-mcp-java
This server has no published package — it is built from source. Open the repository and follow its README.
▸ github.com/Log-LogN/langfuse-mcp-javaFAQ
Is log-logn/langfuse-mcp-java MCP free?
Yes, log-logn/langfuse-mcp-java MCP is free — one-click install via Unyly at no cost.
Does log-logn/langfuse-mcp-java need an API key?
No, log-logn/langfuse-mcp-java runs without API keys or environment variables.
Is log-logn/langfuse-mcp-java hosted or self-hosted?
Self-hosted: the server runs locally on your machine via the install command above.
How do I install log-logn/langfuse-mcp-java in Claude Desktop, Claude Code or Cursor?
Open log-logn/langfuse-mcp-java 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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