A
licenseNot graded
qualityA
maintenanceEnables prompt versioning, evaluation, and management via MCP, CLI, and REST APIs, with support for multiple LLM providers.
MIT
mlflow-mcp-server
by us-all
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TDQS
B3.2/5.0
Scored across 82 tools
Disambiguation5/5
Every tool targets a distinct resource-action pair (e.g., create-experiment vs create-run vs create-logged-model). Even similar functions like search-runs and search-runs-by-tags are clearly differentiated by the 'by-tags' suffix. There is no ambiguity between any two tools.
Naming Consistency5/5
All tool names follow a consistent verb-noun pattern using lowercase and hyphens (e.g., delete-run-tag, set-experiment-tag, search-registered-models). The pattern is uniform across all 82 tools, with no mixing of conventions or unexpected formats.
Tool Count2/5
With 82 tools, the count far exceeds the recommended 3-15 range for a typical server. While MLflow is a broad platform, the fine-grained separation into individual operations (e.g., separate log-metric, log-param, log-batch) results in an overwhelming surface that could hinder agent efficiency, despite the presence of a search-tools helper.
Completeness5/5
The tool set covers the entire MLflow lifecycle: experiments, runs, model registry (including aliases and versions), logged models, prompt optimization, traces with assessments, webhooks, and compound aggregate tools like summarize-experiment. There are no obvious dead ends or missing CRUD operations for the domain.
Maintenance
ActivityInactive
ResponsivenessNo issues