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OctoTrip

OctoTrip Rental Cars

Official
by OctoTrip
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

92%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.5

  • Disambiguation5/5

    Only one tool exists, so there is no risk of confusing it with others. The tool's purpose is clearly distinct.

    Naming Consistency5/5

    The single tool is named 'search', which is clear and consistent with its function. No pattern to evaluate, but naming is appropriate.

    Tool Count2/5

    One tool for a rental car domain is too few. The server's scope implies a need for booking and management tools, making the count inappropriately low.

    Completeness2/5

    The server only offers searching, missing essential operations like booking, modifying, or canceling reservations. This is a significant gap that prevents completing typical rental car workflows.

  • Average 4.6/5 across 1 of 1 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 21 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • Tools from this server were used 2 times in the last 30 days.

  • This repository includes a glama.json configuration 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

  • Behavior5/5

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

    Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds significant context: queries external APIs in real time, returns price-ranked results grouped by SIPP/category, may include affiliate links, and explicitly states what it does not do (book, modify, charge, store). No contradiction.

    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 three sentences, efficient, and front-loaded with the main action. It could be slightly more terse (first sentence is long), but overall 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.

    Completeness5/5

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

    Given the complexity (9 parameters, real-time external API, output schema exists), the description covers the tool's purpose, usage context, behavioral traits, and explicitly states limitations. It does not need to detail return values due to output schema presence.

    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% and each parameter has detailed descriptions (e.g., age surcharge, currency defaults). The description only summarizes parameters without adding new meaning beyond what the schema provides. 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 clearly states that the tool searches live rental-car offers, specifies key parameters (location, dates, optional fields), and distinguishes it from other travel searches (flights, hotels). Verb 'searches' and resource 'live rental-car offers' are specific and unambiguous.

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

    Usage Guidelines5/5

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

    Explicit guidance is provided: 'Use this when the user wants to compare available rental cars... Do not use it for flights, hotels... unless the user has car-rental intent.' This clearly defines when to use and when not to, despite no sibling tools.

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

GitHub Badge

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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