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rkram90

My UV MCP Server

by rkram90
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Python

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'add_numbers' has a single, clearly defined purpose that cannot be confused with any other tool in this set.

    Naming Consistency5/5

    The single tool name 'add_numbers' follows a clear verb_noun pattern, and with only one tool, there is no inconsistency to evaluate. The naming is straightforward and predictable in isolation.

    Tool Count2/5

    A single tool is too few for most practical server purposes, as it severely limits functionality and scope. This feels thin and inadequate for handling any meaningful domain beyond a trivial operation, indicating a poor fit for typical MCP server use cases.

    Completeness1/5

    The server's purpose is unclear from the single tool, making it impossible to assess coverage meaningfully. With only a basic arithmetic operation, there are significant gaps in any plausible domain, rendering the tool surface severely incomplete for practical agent workflows.

  • Average 3.8/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden. It mentions the basic behavior (addition and returning result) but lacks details on error handling, performance, or side effects. This is a significant gap for a tool with no annotation coverage.

    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 a single, efficient sentence that front-loads the purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function.

    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?

    Given the tool's low complexity (simple arithmetic), no annotations, and an output schema present, the description is mostly complete. It covers the basic operation but could improve by addressing behavioral aspects like error cases or limitations.

    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%, but the description clarifies that parameters 'a' and 'b' are integers to be added, adding meaning beyond the schema's type definitions. However, it doesn't specify constraints like range or format, keeping it from a perfect score.

    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?

    The description clearly states the specific action ('Add two integers') and the outcome ('return the result'), with no sibling tools to differentiate from. It uses precise verbs and identifies the resource (integers) without being tautological.

    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?

    The description implies usage for adding two integers but provides no explicit guidance on when to use this tool versus alternatives, prerequisites, or exclusions. With no sibling tools, the context is straightforward but lacks detailed instructions.

    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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  • Confirm that there are no obvious security issues.
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