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cr2007

Wordle MCP (Python)

by cr2007

Server Quality Checklist

42%
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  • This repository includes a README.md file.

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  • Latest release: v0.1.0

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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  • This server provides 1 tool. View schema
  • No known security issues or vulnerabilities reported.

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Tool Scores

  • Behavior3/5

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

    Annotations declare readOnlyHint=true, establishing this is a safe read operation. The description adds the date range constraint (2021-05-19 to 23 days future), which is useful behavioral context not captured in annotations. It does not disclose rate limits, error cases, or return format.

    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?

    Single sentence contains essential information (verb, resource, constraint) with minimal waste. Minor grammatical awkwardness ('to 23 days future' rather than 'into the future') slightly impacts clarity but not comprehension.

    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?

    For a single-parameter read-only tool, the description covers the essential operation and input constraints. However, it omits the expected date format (ISO 8601 implied by default value but not stated) and provides no information about the return value structure (absent output_schema).

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage, the description compensates well by clarifying that the parameter represents a 'particular date' and specifying its valid range ('between 2021-05-19 to 23 days future'), giving clear semantics to the target_date parameter despite the schema providing only type and title.

    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 uses a specific verb ('Fetches') and resource ('the Wordle'), and adds the critical scope constraint (specific date range). While it doesn't explicitly say 'solution' or 'answer', 'Fetches the Wordle' combined with the tool name makes the purpose clear.

    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 the valid date range constraint ('between 2021-05-19 to 23 days future'), which functions as an input guideline. However, it lacks explicit 'when to use' guidance, error handling notes, or alternatives (though no siblings exist).

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

GitHub Badge

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  • Evaluate tool definition quality.

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How to add a LICENSE?

Please follow the instructions in the GitHub documentation.

Once GitHub recognizes the license, the system will automatically detect it within a few hours.

If the license does not appear on the server after some time, you can manually trigger a new scan using the MCP server admin interface.

How to sync the server with GitHub?

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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.

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