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qilincoder

PyCopilot

by qilincoder
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Python

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are clearly distinct - one adds and one multiplies. There is no ambiguity between them.

    Naming Consistency4/5

    Both tools follow a consistent verb-only naming convention (add, multiply), which is simple and predictable.

    Tool Count3/5

    Two tools feels very thin, even for a simple calculator server. A set like this could reasonably include subtract, divide, or other basic operations, but for its very narrow apparent scope it's borderline.

    Completeness3/5

    The tool surface is extremely limited, covering only two basic arithmetic operations. There's no indication of a broader domain purpose that would justify additional tools, but for a calculator-style server this is somewhat acceptable yet still quite thin.

  • Average 3.6/5 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

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

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

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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 carries the burden. It states the operation (addition) and return value (the sum). For a pure, stateless arithmetic operation, there is minimal risk or side-effect behavior to disclose further. The description covers the essential behavioral contract adequately.

    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?

    Two short, informative sentences with zero waste. Every word earns its place, and the key information is front-loaded in the first sentence.

    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?

    This is a simple tool with 2 parameters, full schema coverage, and no output schema. For an arithmetic operation like this, the description is complete - it states what it does and what it returns. The low complexity means no further documentation is necessary.

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

    Parameters3/5

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

    Schema coverage is 100%, with both parameters (a and b) having descriptions in the schema. The description mentions 'a and b' which maps directly to the parameters, but adds no additional semantic detail beyond the schema's own documentation. Baseline 3 is appropriate.

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

    Purpose4/5

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

    The description has a specific verb ('add'), clear resource ('two numbers'), and states the result ('Returns the sum'). It clearly communicates the operation. It doesn't explicitly distinguish from the sibling 'multiply' tool, but the verb 'add' makes the difference self-evident.

    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 its usage: use it when adding numbers. It doesn't explicitly state when not to use it or mention the sibling 'multiply' alternative. However, given the simplicity of the tool, the implied usage is reasonably clear and adequate.

    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 carries the full burden of behavioral disclosure. 'Multiply two numbers together' is a pure computation with no side effects implied, which is reasonably transparent. However, it doesn't state return type, precision handling, or whether very large numbers have special behavior. The simplicity of the operation makes this acceptable but not thorough.

    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 4-word sentence that is exceptionally economical. Every word earns its place with no fluff, no redundancy, and no wasted structure. For a tool of this simplicity, this is ideal conciseness.

    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?

    This is a trivially simple tool: 2 parameters, both required, both documented at 100%, no output schema, no nested objects, no annotations. Given this minimal complexity, the single-sentence description is largely sufficient for an agent to select and invoke it correctly. The only minor gap is the lack of a defined return format, but for a pure arithmetic operation this is easily inferred.

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

    Parameters3/5

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

    Schema description coverage is 100%, meaning both parameters (a and b) are already fully described as 'The first number' and 'The second number'. The description adds minimal value beyond the schema - it just restates that two numbers are multiplied. Baseline 3 is appropriate since the schema does the heavy lifting and the description confirms the relationship between the two parameters.

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

    Purpose4/5

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

    The description 'Multiply two numbers together' clearly states the verb (multiply), resource (two numbers), and outcome. It's distinct from its sibling 'add' which would be used for addition. A minor gap is that it doesn't specify any return behavior or edge cases, but the purpose is unambiguous.

    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 context (multiplying two numbers), and the sibling 'add' provides some implicit differentiation for arithmetic operations. However, there's no explicit guidance on when to choose this over add, no mention of number type constraints (integers vs floats), overflow considerations, or ordering implications.

    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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