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goodfel10w

welcome-text-generator

by goodfel10w
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TypeScript
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.2

  • Disambiguation5/5

    Each tool has a distinct purpose: extracting data, generating welcome text, and listing extracted data. No overlapping functionality.

    Naming Consistency5/5

    All tools use a consistent verb_noun pattern in snake_case, making them predictable and easy to understand.

    Tool Count5/5

    Three tools is an appropriate number for a focused server, covering extraction, generation, and listing without unnecessary bloat.

    Completeness4/5

    The core workflow of extracting data and generating welcome texts is covered. However, lacking update or delete operations for extracted data is a minor gap.

  • Average 3.1/5 across 3 of 3 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.

  • 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 must disclose behavioral traits. However, it only states the output type ('welcome text') without mentioning side effects, permissions, rate limits, or whether it is read-only or destructive. The lack of specification is a significant gap.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence, which is concise. However, it is under-specified; it would benefit from additional context while remaining brief. It is not verbose, but minimalism sacrifices completeness.

    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 complexity (nested objects, many subproperties, no output schema), the description is insufficient. It does not explain return values, what the 'module template system' is, or how to structure inputs properly.

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

    Parameters1/5

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

    Schema description coverage is 0% (no descriptions on top-level parameters), and the tool description adds no information about the parameters. It does not explain what 'data' or 'moduleOptions' are or how to use them, despite the schema having nested objects and multiple subproperties.

    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 the verb ('generiert' = generates) and resource ('strukturierten Willkommenstext' = structured welcome text), and the tool name aligns with this purpose. It is distinct from sibling tools like 'extract_from_text' and 'list_extracted_data', which are about extraction and listing, not generation.

    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 does not mention prerequisites, when not to use it, or compare with sibling tools.

    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?

    No annotations are present, so the description carries the full burden. It does not disclose whether the tool is read-only, modifies data, or requires any special permissions. The term 'extracts' implies a read operation, but this is not explicitly stated, and no other behavioral traits are mentioned.

    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 (single sentence) and front-loaded with the essential action. However, it is quite short and could benefit from additional structure or contextual details without sacrificing conciseness.

    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 simplicity (one parameter, no output schema, no annotations), the description is adequate but not fully complete. It explains what the tool does but omits details about the output format or any constraints on input text.

    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% (one parameter 'text' with a clear description in German). The tool description does not add any meaning beyond the schema, so 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 states the verb 'extracts' and resource 'structured data from shared text', making the tool's purpose clear. The sibling tools (generate_modular_welcome_text, list_extracted_data) are distinct in name and function, so differentiation is implicit. However, the description does not specify what kind of structured data is extracted, leaving some ambiguity.

    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?

    No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, context, or exclusions. The single-sentence description lacks any usage direction.

    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?

    No annotations provided; description only says 'lists' without detailing output structure, pagination, or any limits. Minimal behavioral disclosure.

    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?

    Single sentence, no redundant words, front-loaded. Very concise.

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

    Completeness4/5

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

    For a zero-parameter, no-output-schema tool, description adequately states what is listed. Could mention relation to sibling tools but not necessary.

    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?

    No parameters, so baseline 4. Description adds no param info, but schema coverage is 100% (empty), so it's adequate.

    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?

    Description states 'lists all extracted and saved employee data' - specific verb and resource, clearly distinguishing from siblings (extract_from_text, generate_modular_welcome_text).

    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?

    No explicit guidance on when to use this vs alternatives. Only implied it should be used after extraction, but no when-not or alternative mention.

    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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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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