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VinodNallavade

usefastmcp

Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    roll_dice and add_number are completely distinct in purpose and behavior. There is no ambiguity about which tool to select for a given task.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern: roll_dice and add_number. The naming style is uniform and predictable.

    Tool Count3/5

    With only two tools, the server feels quite thin. While each tool has a basic utility purpose, the set is minimal and barely provides enough surface to be considered a coherent collection.

    Completeness2/5

    There is no clear domain or lifecycle being covered; the tools are unrelated utilities. Obvious complementary operations (e.g., subtract, multiply, or dice with custom sides) are absent, leaving the surface feeling arbitrary and incomplete.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 3 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.

Tool Scores

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the core behavior (arithmetic addition) and that a result is returned, but it does not specify return type, edge-case behavior, or the absence of side effects beyond what is implied.

    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 short sentence that front-loads the action and contains no unnecessary words. Every phrase contributes to understanding the tool's purpose.

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

    Completeness3/5

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

    For a trivial pure function, this is nearly sufficient, but without an output schema it does not explicitly state that the returned result is the numeric sum or what type it is. It is adequate but leaves minor ambiguity about return behavior.

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

    Parameters2/5

    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 only says '2 numbers' and does not explain the roles of the first and second parameters, their constraints, or ordering, leaving the agent to infer from parameter names alone.

    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 states a specific verb ('Add'), a clear target resource ('2 numbers'), and an outcome ('returns result'). It is unambiguous and clearly distinct from the sibling tool roll_dice, which involves randomness.

    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 use case is implied: an agent would choose this when needing the sum of two integer inputs. However, there is no explicit when-to-use or when-not-to-use guidance, and no mention of alternatives or exclusions.

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

  • Behavior3/5

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

    No annotations are provided, so the description must carry the behavioral disclosure burden. It discloses the output range and the context, but does not explicitly state that the result is random, which is a meaningful behavioral trait for a dice-roll tool.

    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, short sentence with no unnecessary content beyond a minor filler phrase. The key purpose and trigger are front-loaded and immediately understandable.

    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?

    For a zero-parameter tool with no output schema, the description covers the essential return range and use case. It is sufficient for simple invocation, though explicitly mentioning randomness would make it fully complete.

    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?

    The tool has zero parameters, so the baseline is 4. The description correctly focuses on behavior rather than parameters, and no parameter documentation is needed.

    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?

    States a specific action and resource: returns a number between 1 and 6 when rolling a dice. This clearly distinguishes it from the sibling add_number, which has a completely different purpose.

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

    Usage Guidelines4/5

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

    The phrase 'when asked to roll a dice' gives a clear usage condition. It does not explicitly mention alternatives or exclusions, but the sibling tool is unrelated and the intended trigger is obvious.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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