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musantro

iRacing MCP

by musantro
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves general profile statistics, the other provides historical iRating data for a specific category. There is no ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern using snake_case and the 'get_iracing_' prefix, making them predictable and easy to understand.

    Tool Count2/5

    With only 2 tools, the server is severely under-scoped for the iRacing domain, which typically requires many more tools for operations like searching races, managing subscriptions, or accessing car/track data.

    Completeness1/5

    The tool surface is extremely limited, covering only profile stats and iRating history. Critical operations like race listing, car/track data, league management, and session results are entirely missing, making it incomplete for practical use.

  • Average 3.7/5 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 MIT License.

  • 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

  • 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 discloses that the tool retrieves data but not behavioral traits like authentication needs, rate limits, data freshness, or whether it's a read-only operation. This leaves key behavioral information missing.

    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 sentences, front-loaded with the core action. Every word adds value; no extraneous content.

    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 no parameters and no output schema, so the description must specify return values. It mentions license, iRating, and career stats but does not list specific fields or structure. For a simple profile retrieval, this is adequate but not 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 input schema has zero parameters, so the baseline is 4 per rules. The description does not add parameter-level detail because none exist, but it correctly implies no arguments are 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?

    The description clearly states the verb 'Get' and the resource 'iRacing profile statistics', and lists the types of data (license level, iRating, career stats). The sibling tool get_irating_chart suggests a different focus (charts), so the distinction is implicit.

    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 on when to use this tool versus the sibling get_irating_chart or any prerequisites. The description only states what it does without contextual usage advice.

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

  • Behavior2/5

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

    No annotations are provided, so the description must fully disclose behavioral traits. It only states it retrieves iRating history data but does not mention whether it is read-only, any rate limits, data range, or what happens if the category is invalid. This lack of transparency for a simple retrieval tool is a notable gap.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is four sentences: the first two state the purpose, the third adds charting context, and the fourth lists the category mapping. It is front-loaded and each sentence provides useful information without redundancy. Slightly verbose due to repetition between sentence 1 and 2, but still concise overall.

    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?

    Given the simplicity of the tool (one parameter, no output schema), the description is adequately complete for basic usage. It explains what the tool does and what the parameter means. However, it does not describe the format or structure of the returned data, which would be helpful for charting without an output schema. A bit more detail on the return type (e.g., array of {date, irating} objects) would increase completeness.

    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 input schema has 0% description coverage, so the description must compensate. It does so by explicitly mapping integer values to category names (e.g., '1 is oval'), which adds significant meaning beyond the schema's bare type 'integer'. This allows the agent to correctly select the category. However, it could also mention that the parameter is required and that it corresponds to a license category.

    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 'Get the iRating data for a specific license category' and 'Retrieves the user's iRating history data', specifying both the verb and the resource. It distinguishes itself from the sibling tool 'get_iracing_profile_stats' by focusing on historical data for charting, implying a different use case (history vs. profile stats).

    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 explains the tool is for retrieving historical iRating values 'for charting', which gives a clear use context. It also provides the mapping of category IDs (1-6), which serves as a usage guideline. However, it does not explicitly state when not to use this tool or compare it directly with the sibling, though the context is strong enough.

    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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