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trustcarinfo

mcp-server-trustcar

Official
by trustcarinfo
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JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.2

  • Disambiguation5/5

    Each tool targets a clearly distinct action: lookup retrieves a single plate's details, recent lists plates, and post creates a review. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Two tools use verb_noun (lookup_plate, post_review) but recent_plates uses adjective_noun, deviating from the pattern. The names are still intuitive and readable.

    Tool Count5/5

    Three tools is well-scoped for a niche service focused on licence plate lookup, listing, and review posting. Each tool serves a distinct need without excess.

    Completeness4/5

    The core read and write operations are covered: lookup provides full details and reviews, recent lists plates, and post creates a review. Minor gaps like updating or deleting reviews are not essential for the server's purpose.

  • Average 4.2/5 across 3 of 3 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 is passing

  • This repository is licensed under MIT License.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries the burden. It discloses the read-only nature via 'List' and adds the useful caveat that vehicle make/model are included only where known, setting expectations for the output.

    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?

    One sentence, front-loaded with the main action, and includes a relevant qualifier without waste.

    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 simple list tool with a single optional parameter, the description provides enough context for invocation: it names the data source, the type of items, and an output detail. The absence of an output schema is mitigated by the straightforward nature of the tool.

    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?

    The schema already documents the limit parameter with range and default (100% coverage), so the description need not elaborate; it adds no additional meaning for parameters.

    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 uses the specific verb 'List' with the resource 'recently active plates' on trustcar.info, clearly distinguishing from siblings lookup_plate and post_review.

    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 for listing recent plates but does not explicitly contrast with lookup_plate or post_review, leaving the when-to-use decision to inference.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read-only lookup operation and names the external site, but it does not disclose potential error conditions, network dependencies, or rate limits. The description is adequate but does not go beyond basic expectations.

    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, front-loaded sentence that efficiently conveys purpose and output fields without redundancy. Every phrase earns its place.

    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?

    For a tool with one parameter and no output schema, the description fully covers what the tool does and what it returns (region, vehicle details, rating, reviews). The external source is specified. This is complete for a simple lookup operation.

    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% for the single 'plate' parameter, which already includes a format description and example. The tool description adds no semantically new parameter information, keeping it at the baseline 3.

    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 uses the specific verb 'Look up' and names the resource ('Ukrainian licence plate') and source ('trustcar.info'), listing the returned data (region, vehicle, rating, reviews). This clearly distinguishes it from siblings: recent_plates (listing) and post_review (writing).

    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 does not explicitly state when to use this tool instead of recent_plates or post_review, but the read/lookup intent is clear from the verb and data fields. The context is sufficient, but it lacks explicit exclusion statements.

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

  • Behavior5/5

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

    With no annotations, the description carries the full burden and reveals several non-obvious behaviors: OAuth scope discovery, AI-agent-labelled output, separate section, and no impact on the human rating. This goes well beyond what the input schema provides.

    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 two sentences, front-loaded with the action, and followed by necessary authentication and behavioral caveats. Every sentence earns its place with no wasted words.

    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 simple 3-parameter write operation with no output schema, the description explains purpose, authentication, and the special output treatment. It does not mention response/error details, but the essential context for safe and correct use is present.

    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?

    The input schema covers all three parameters (plate, rating, text) with descriptions, achieving 100% schema coverage. The description adds no additional parameter-level meaning, so a baseline score of 3 is appropriate.

    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 begins with 'Post a licence-plate review', a specific verb+resource action. It clearly distinguishes from the sibling read tools (lookup_plate, recent_plates) by focusing on the write/submission operation.

    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 clearly states the authenticated-user requirement and OAuth 'write' scope, giving the context for when this tool applies. It does not explicitly name alternatives or when-not-to-use, but the sibling read tools make the write-vs-read distinction 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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