langfuse-mcp-java
Related Servers
Alternatives to langfuse-mcp-java
No user-submitted related servers found.
Related Servers
- AlicenseCqualityAmaintenanceMCP server for Langfuse observability. Query traces, debug exceptions, analyze sessions, and manage prompts and datasets for your LLM applications.48105MIT
- AlicenseBqualityDmaintenanceEnables querying Langfuse analytics, cost metrics, and usage data across multiple projects. Provides tools for trace analysis, model/service cost breakdowns, and daily usage trends through natural language queries.2469 npmMIT
- AlicenseBqualityDmaintenanceModel Context Protocol server for Langfuse observability. Query traces, analyze accuracy, detect failures, track costs, debug latency, manage prompts and datasets.582MIT
- AlicenseAqualityCmaintenanceFetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.31MIT

spanlens-mcpofficial
AlicenseAqualityCmaintenanceMCP-native LLM observability. Query your Spanlens traces, stats, cost anomalies, and savings from Cursor, Claude Desktop, or any MCP client. Open source (MIT).713MIT- AlicenseNot gradedqualityBmaintenanceA read-only MCP server that enables agents to query and analyze Langfuse observability data, including traces, sessions, observations, scores, and metrics.MIT
TDQS
Scored across 55 tools
Most tools are clearly distinct by resource and action, such as create_annotation_queue vs. get_annotation_queue. However, some overlap exists in read operations like fetch_trace and get_exception_details, which both retrieve trace details but with different filters, potentially causing confusion. The descriptions help clarify, but the sheer number of tools (55) increases the risk of misselection.
Tool names follow a highly consistent verb_noun pattern throughout, such as create_annotation_queue, get_annotation_queue, list_annotation_queues, and update_annotation_queue_item. All tools use snake_case without deviation, making the naming predictable and easy to understand. This consistency aids in agent navigation and reduces cognitive load.
With 55 tools, the count is excessive for an MCP server, far beyond the typical well-scoped range of 3-15 tools. This large number suggests over-fragmentation, such as having separate tools for fetch_trace and get_exception_details, which could be consolidated. It will likely overwhelm agents and increase complexity unnecessarily.
The tool set provides comprehensive CRUD and lifecycle coverage for the Langfuse domain, including traces, sessions, datasets, prompts, models, scores, annotation queues, and LLM connections. There are no obvious gaps; every major resource has create, read, update, and delete operations, along with specialized tools for analytics and filtering, ensuring agents can handle full workflows without dead ends.