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Traves-Theberge

Word of the Day MCP Server

by Traves-Theberge
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation4/5

    The two tools have distinct purposes: get_random_word fetches a random word with its definition, while get_word_definition retrieves detailed information for a specific word. There is minimal overlap since one is for random discovery and the other for targeted lookup, though both involve word definitions, which could cause slight confusion.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern (get_random_word and get_word_definition), using the same verb 'get' and clear, descriptive nouns. This consistency makes the tools easily predictable and readable.

    Tool Count3/5

    With only 2 tools, the server feels thin for a 'Word of the Day' domain, as it lacks operations like listing words, saving favorites, or exploring related terms. While the core functionality is present, the scope is limited, bordering on minimal.

    Completeness3/5

    The server covers basic word retrieval and definition lookup, but there are notable gaps for a comprehensive word-of-the-day experience. Missing operations include browsing word lists, historical words, or user interactions, which could limit agent workflows in this domain.

  • Average 3/5 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 of behavioral disclosure. It states the tool returns a random word with its definition, but doesn't cover other behavioral traits such as rate limits, authentication needs, error handling, or whether the word changes per call. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operation.

    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 a single, efficient sentence that clearly states the tool's purpose without unnecessary words. It's front-loaded with the core function, making it easy to grasp quickly. However, it could be slightly more structured by explicitly noting the optional parameter, but overall it's concise and well-formed.

    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 (one optional parameter) and no output schema, the description is minimally adequate. It covers what the tool does but lacks details on behavioral aspects like how randomness is implemented or what the return format includes. Without annotations or output schema, more context on the response structure would improve completeness, but it's not entirely incomplete.

    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 'difficulty' fully documented in the schema (including enum values and default). The description doesn't add any meaning beyond this, as it doesn't mention parameters at all. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

    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: 'Get a random word with its definition for word of the day'. It specifies the verb ('Get'), resource ('random word'), and additional output ('definition'), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'get_word_definition', which likely fetches definitions for specific words rather than random ones, so it falls short of a perfect 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 doesn't mention the sibling tool 'get_word_definition' or clarify scenarios where one might prefer a random word over a specific lookup. Usage is implied by the phrase 'word of the day', but this is vague and lacks explicit context or exclusions.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but lacks details on traits like rate limits, error handling, or response format. This is a significant gap for a tool that interacts with an external API.

    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, efficient sentence that front-loads the key information (what the tool does) without any wasted words. It is appropriately sized for the tool's complexity.

    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 moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage context, or output, leaving gaps that could hinder effective tool selection.

    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, documenting both parameters ('word' and 'language') clearly. The description adds no additional meaning beyond what the schema provides, such as examples or constraints, so it 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.

    Purpose4/5

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

    The description clearly states the action ('Get') and the resources involved ('definition, pronunciation, and meanings of a word'), making the purpose evident. However, it doesn't explicitly differentiate from the sibling tool 'get_random_word', which appears to serve a different purpose (fetching random words rather than definitions).

    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, such as the sibling 'get_random_word'. It mentions the Dictionary API but doesn't specify contexts or exclusions, leaving usage decisions unclear.

    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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