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isaiahbjork

Allyson MCP Server

by isaiahbjork
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JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.5

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The single tool has a clear purpose.

    Naming Consistency5/5

    The single tool name 'generate_svg_animation' uses a clear snake_case verb_noun pattern. Consistency is not an issue with one tool.

    Tool Count3/5

    The server has only one tool, which is borderline for a server with a generic name. However, if the server's sole purpose is generating SVG animations, the count is justifiable but feels minimal.

    Completeness4/5

    The single tool covers the primary operation of generating an SVG animation from source and prompt. Minor gaps exist, such as no tool for listing or managing animations, but for a focused tool it is acceptable.

  • 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under Apache 2.0.

  • 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?

    With no annotations, the description bears full responsibility for disclosing behavioral traits. It omits critical details such as whether the tool modifies the input file, what happens to existing files at output_path, or any requirements for the SVG content. The sparse description leaves much to inference.

    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 extremely concise (one sentence, nine words), which is acceptable for a simple tool. However, it lacks structure (e.g., no front-loading of important constraints or behaviors) and could be slightly expanded to improve usability without becoming verbose.

    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's generative nature, the description is incomplete. It does not explain the output animation format, any side effects, or potential limitations. Without an output schema, the agent receives no information about what the tool returns, leaving a significant gap.

    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 covers 100% of parameters with clear descriptions. The tool description adds no additional meaning beyond the schema, but the schema itself is sufficiently descriptive for each parameter, making the baseline score 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 action 'Generate an SVG animation' and identifies the resources ('from a source file and prompt'). It is specific enough to convey the core functionality without confusion.

    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 (though none are listed), nor does it mention any preconditions or exclusions. It only states what the tool does, not when it should be used.

    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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