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ClearMind1

minio_mcp

by ClearMind1
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

33%
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  • Latest release: v0.1.2

  • Disambiguation2/5

    The two tools have overlapping purposes—both upload files to MinIO—with only minor distinctions in input format (base64 vs. local file). An agent could easily misselect between them if the input format isn't clearly specified, as the core action is identical. The descriptions help differentiate, but the fundamental overlap in functionality creates ambiguity.

    Naming Consistency5/5

    The tool names follow a perfectly consistent verb_noun pattern: both start with 'upload', followed by a descriptor ('base64' or 'file'), and end with 'to_minio'. This predictable naming scheme makes it easy for agents to understand and select tools without confusion.

    Tool Count2/5

    With only 2 tools, the server feels severely under-scoped for a MinIO integration, which typically involves operations like listing, downloading, deleting, or managing buckets. The count is too low to cover the basic CRUD/lifecycle needs of file storage, limiting agent functionality.

    Completeness1/5

    The tool surface is extremely incomplete for a MinIO server, covering only upload operations with no ability to retrieve, list, delete, or manage files or buckets. This creates significant gaps that will cause agent failures in most file storage workflows, as agents cannot perform basic operations beyond uploading.

  • Average 2.7/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

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