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ramsrk7

Project TTV MCP

by ramsrk7
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Python

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The purpose of 'add' is clear and distinct from any hypothetical alternatives, so agents can easily select it.

    Naming Consistency5/5

    The tool name 'add' is a clear, action-oriented verb that accurately describes its function. Although there is no noun component, the convention is consistent across the single tool, making it predictable.

    Tool Count1/5

    A single 'add' function is an extreme mismatch for an MCP server, which typically provides a coherent set of tools. The server appears to be a trivial utility with no broader scope.

    Completeness1/5

    The server offers only addition, with no support for other arithmetic operations like subtraction, multiplication, or division. This is a severely incomplete surface for any arithmetic or project-related purpose.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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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  "$schema": "https://glama.ai/mcp/schemas/server.json",
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  ]
}

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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

  • Behavior5/5

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

    The description discloses the function's behavior and its return value ('Returns sum'). For a pure arithmetic operation, this is fully transparent. No additional behavioral traits like side effects or permissions are relevant.

    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, using brief statements for the operation, parameters, and return value. Every element serves a purpose with no extraneous text.

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

    Completeness5/5

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

    Given the tool's simplicity, the description is complete. It covers the operation, parameters, and return value, and no output schema is needed to understand the result.

    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?

    The description lists 'a: int' and 'b: int', which simply repeats the input schema's type information without adding semantic meaning beyond labeling them as numbers. The schema coverage is 0%, so the description does not compensate for the lack of parameter descriptions.

    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 'Function to add two numbers' which clearly identifies the verb (add) and resource (two numbers). It is specific and unambiguous, though it doesn't need to differentiate from siblings as none exist.

    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 description doesn't explicitly state when to use this tool, but the phrase 'Function to add two numbers' implies the use case is whenever addition is needed. There are no alternatives or exclusions mentioned, which is acceptable given the simplicity of the function.

    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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