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BACH-AI-Tools

Cocktail By Api Ninjas

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as accessing the Cocktail API with specific parameter requirements.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions.

    Tool Count2/5

    One tool is too few for a server named 'Cocktail By Api Ninjas', which suggests a domain with multiple potential operations (e.g., search by name, ingredient, category, random cocktail). This minimal surface will likely cause agent failures due to limited functionality.

    Completeness1/5

    The server is severely incomplete for its apparent cocktail domain. It only offers a single generic endpoint without clear CRUD or lifecycle coverage (e.g., no distinct tools for searching, filtering, or retrieving detailed cocktail information), leaving significant gaps in functionality.

  • Average 2.8/5 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 is passing
  • 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 mentions the API endpoint but doesn't disclose behavioral traits like rate limits, authentication needs, error handling, or response format. The description adds minimal context beyond the parameters, leaving significant gaps in understanding how the tool behaves operationally.

    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 concise with two sentences that directly address the tool's usage and parameter requirements. It's front-loaded with the API endpoint context and avoids unnecessary details. However, it could be slightly more structured by explicitly stating the action (e.g., 'search cocktails').

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

    Completeness2/5

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

    Given the tool has no annotations, no output schema, and 2 parameters, the description is incomplete. It covers parameter constraints but lacks information on behavioral aspects, response format, and error handling. For a tool interacting with an external API, this leaves the agent with insufficient context to use it effectively.

    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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds value by clarifying that at least one parameter must be set, which isn't in the schema (since required is empty), but it doesn't provide additional meaning beyond what the schema offers. This meets the baseline for high schema coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool accesses an API endpoint for cocktails, which gives a general purpose, but it's vague about the specific action (e.g., search, retrieve, list). It mentions parameters but doesn't clearly distinguish the verb+resource combination, such as 'search cocktails by name or ingredients.' With no siblings, differentiation isn't needed, but the purpose lacks specificity.

    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 provides implied usage by stating that either 'name' or 'ingredients' must be set, which offers some context for when to use the tool. However, it lacks explicit guidance on when to choose one parameter over the other, alternatives, or exclusions. With no sibling tools, this is adequate but not comprehensive.

    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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  • Evaluate tool definition quality.

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