F
licenseNot graded
qualityA
maintenanceEnables AI agents and MCP clients to query Langfuse trace data for debugging and observability, with tools for traces, sessions, exceptions, routing decisions, prompts, datasets, scores, and metrics.
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langfuse-mcp-java
by Log-LogN
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TDQS
B3.2/5.0
Scored across 55 tools
Disambiguation4/5
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.
Naming Consistency5/5
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.
Tool Count2/5
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.
Completeness5/5
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.