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BACH-AI-Tools

Thesaurus By Api Ninjas

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
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 confusion or overlap between tools. The tool's purpose is clearly defined as returning synonyms and antonyms for a given word, making it distinct by default.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'v1thesaurus' follows a single pattern without any deviations or mixing of conventions.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and scope. While it might suffice for a simple thesaurus lookup, it feels thin and lacks depth for broader language processing tasks.

    Completeness3/5

    The tool covers the basic lookup of synonyms and antonyms, which is the core function of a thesaurus. However, there are notable gaps such as no support for related words, word definitions, examples, or advanced language features, making the surface incomplete for comprehensive language analysis.

  • 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 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.

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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 describes the return value (lists of synonyms and antonyms) but doesn't mention any behavioral traits such as rate limits, authentication requirements, error handling, or whether it's a read-only operation. The description is functional but lacks important operational context.

    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 extremely concise and front-loaded, consisting of a single sentence that directly states the tool's function. There is no wasted language, and it efficiently communicates the core purpose without unnecessary details.

    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 tool's low complexity (single parameter, no annotations, no output schema), the description is minimally adequate. It explains what the tool returns but doesn't cover behavioral aspects like error cases or performance. Without an output schema, it could benefit from more detail on return format, but it meets basic requirements for a simple lookup 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 input schema has 100% description coverage, with the single parameter 'word' documented as 'word to look up.' The description adds no additional meaning beyond this, as it only reiterates that it takes 'a given word.' Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

    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 tool's purpose: 'Returns a list of synonyms and a list of antonyms for a given word.' It specifies the verb ('Returns'), resource ('list of synonyms and antonyms'), and target ('given word'). However, since there are no sibling tools, it doesn't need to differentiate from alternatives, so it doesn't reach the highest score.

    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?

    The description provides no guidance on when to use this tool versus alternatives. It states what the tool does but offers no context about when it's appropriate, what prerequisites might be needed, or any limitations. Without sibling tools, explicit alternatives aren't required, but general usage context is missing.

    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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