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utenadev

mcp-mistral-queue

by utenadev
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of ambiguity between tools.

    Naming Consistency5/5

    With a single tool, naming is trivially consistent; the name 'ask_mistral' follows a clear verb_noun convention.

    Tool Count3/5

    A single tool for a queue-based system feels too minimal; typical queue management would require additional tools for status checking or cancellation.

    Completeness2/5

    The tool covers only the submission aspect; missing queue status, priority management, and cancellation capabilities create significant gaps.

  • Average 4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 33 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • 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.

Tool Scores

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description must fully convey behavioral traits. It discloses queuing, rate limiting, and parameter constraints (e.g., system_prompt only effective with prompt). However, it does not discuss error handling, idempotency, or potential side effects, leaving gaps.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is extremely concise: a single sentence followed by a clear bullet list of arguments. No extraneous information; every sentence serves a purpose. The key behavior is front-loaded.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    With 5 parameters and no sibling tools, the description covers all parameters and the return type (response text). It mentions queuing and rate control. An output schema exists, so detailed return documentation is unnecessary. Minor gaps include lack of examples or error scenarios, but overall complete for this complexity.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate. It explains each parameter's purpose and constraints: priority values (1-3), system_prompt condition, model default, etc. This adds significant value beyond the schema's type definitions, though more detail on message array structure could be included.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    Purpose is explicitly stated: 'Calls Mistral API with queuing and rate limit control.' The verb 'call' and resource 'Mistral API' are clear. Since there are no sibling tools, differentiation is not needed, but the description uniquely identifies the tool's functionality.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Usage guidelines are implied through the mention of queuing and rate limiting, suggesting it's designed for rate-managed calls. However, there is no explicit when/when-not guidance or alternatives, which would be beneficial even without siblings.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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  • Evaluate tool definition quality.

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