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bvm-arjun108

GitHub Models Helper

by bvm-arjun108
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

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 ambiguity or overlap with other tools. The purpose of comparing models is clearly described.

    Naming Consistency5/5

    The tool name follows a standard verb_noun pattern ('compare_models'). With a single tool, there is no inconsistency to evaluate.

    Tool Count2/5

    A single tool feels too few for a server named 'GitHub Models Helper', which implies a broader set of utilities. The scope appears overly narrow.

    Completeness2/5

    The server only provides a model comparison tool, leaving obvious gaps such as listing available models or retrieving model details. This significantly limits helper functionality.

  • Average 3.1/5 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",
  "maintainers": [
    "your-github-username"
  ]
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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 of behavioral disclosure. It only states the action without telling whether the call is synchronous, how results are presented, whether it has side effects (e.g., costs, rate limits), or what happens if a model fails. This is a significant gap for a tool that compares models.

    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, direct sentence that starts with the verb 'Compare'. It is concise, front-loaded, and contains no filler words. Every word earns its place.

    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?

    The tool has modest complexity (2 params, no output schema). The description states the core purpose, but without annotations or an output schema it lacks detail on return values and behavioral context. It is a bare minimum viable description, but not rich enough for an agent to fully anticipate the result.

    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%, so the schema already documents both 'prompt' and 'models'. The description adds a minimal semantic hint ('same prompt') but doesn't explain nuances like whether 'models' is an ordered list or how defaults behave. Baseline 3 is appropriate as the schema does the heavy lifting.

    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 clearly states the action ('Compare') and the resource ('responses from different models') along with the context ('same prompt'). However, it doesn't specify what 'compare' means (e.g., side-by-side output, diff, metrics) and there are no sibling tools to differentiate from, so it falls short of a 5.

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

    Usage Guidelines2/5

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

    No guidance is provided on when to use this tool, preconditions, or alternatives. The description is purely functional and doesn't mention any exclusions or scenarios where another tool would be preferred. Since the schema shows it requires a prompt, but that's structural not usage guidance.

    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.
  • Evaluate tool definition quality.

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