A
licenseNot graded
qualityC
maintenanceEnables AI agents to access observability and evaluation data, including run history, span traces, LLM-as-judge evaluation results, and regression reports.
MIT
Open Train MCP
by yidong72
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- FlicenseNot gradedqualityAmaintenanceEnables AI agents and MCP clients to query and debug traces, sessions, observations, and exceptions, plus access prompts, datasets, annotation queues, scores, and metrics from a local server.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents to query traces, sessions, exceptions, prompts, datasets, scores, and metrics for enhanced debugging and observability. Supports local stdio or HTTP deployment, selective tool loading, and read-only mode.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents to query and debug trace, session, observation, and exception data, analyze latency and metrics, and manage prompts, datasets, and annotation queues. Supports selective tool-group loading, compact or file-dump output, and a read-only mode for safer access.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents and MCP clients to debug and observe LLM applications by querying traces, sessions, exceptions, prompts, datasets, scores, and metrics. Supports local stdio/HTTP access, selective tool loading, and read-only operation for safer observability workflows.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents and developers to query traces, sessions, and exceptions for debugging and observability, alongside tools for prompts, datasets, annotation queues, scores, and metrics. Supports selective tool loading and a read-only mode for safer local access from any MCP client.-
TDQS
A3.7/5.0
Scored across 16 tools
Disambiguation4/5
Each tool targets a distinct resource/action, and descriptions clarify boundaries. Minor overlap exists between get_metric_series/get_history and plot_metric/compare_runs, but the set is mostly unambiguous.
Naming Consistency5/5
All tools use snake_case with predictable verb_noun patterns: get_*, list_*, download_*, plot_metric, diagnose_run, compare_runs. No inconsistent casing or vague verbs.
Tool Count4/5
16 tools is slightly above the typical 3–15 range but reasonable for a rich training-observability domain. Each tool appears to earn its place without obvious redundancy.
Completeness4/5
The read-only surface covers runs, metrics, history, logs, files, artifacts, tables, plotting, diagnosis, and comparison well. Minor gaps like full-log download or table discovery exist, but core workflows have no dead ends.
Maintenance
ActivityMaintained
ResponsivenessNo issues