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yahavf6

claude-monet-mcp

by yahavf6
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Server Quality Checklist

67%
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  • Latest release: v1.0.2

  • Disambiguation5/5

    The two tools, clear_sketch and get_sketch, have clearly distinct purposes: one clears the canvas, the other retrieves the current drawing. There is no overlap.

    Naming Consistency5/5

    Both tool names follow the consistent verb_noun pattern (clear_sketch, get_sketch), making their actions predictable.

    Tool Count3/5

    With only 2 tools for a drawing server, the count is slightly low but reasonable if the server's scope is limited to clearing and viewing the sketch made by the user.

    Completeness4/5

    For the apparent purpose of allowing Claude to clear and inspect the user's drawing, the tool surface is mostly complete. However, there might be a minor gap if Claude could benefit from adding elements programmatically.

  • Average 3.8/5 across 2 of 2 tools scored. Lowest: 3.2/5.

    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 present, so the description carries full burden. It states 'Clear the drawing canvas' implying destructive behavior, but lacks details on reversibility, confirmation prompts, or what happens to existing content beyond being cleared.

    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?

    A single concise sentence that directly states the action and purpose. Every word earns its place; no extraneous information.

    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?

    The description is adequate for a simple, zero-parameter action. However, it does not explain the result (e.g., success feedback, return value) or any side effects. With no output schema, slightly more context about what the user can expect after clearing would improve completeness.

    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?

    There are no parameters (0), and schema coverage is 100%. The description adds no parameter information, but none is needed. Baseline 3 applies for high coverage, but given zero parameters, a 4 is warranted as no additional meaning is required.

    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 verb 'Clear' and the resource 'drawing canvas', indicating a specific action. It distinguishes from sibling 'get_sketch' which retrieves data. However, it does not explicitly differentiate the tool's purpose from alternatives.

    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 on when to use or not use this tool. There is no mention of prerequisites, alternatives, or scenarios where this tool should be avoided. Only a single sibling exists but no comparison is provided.

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

  • Behavior4/5

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

    The description accurately describes the tool's behavior as returning an SVG of the current sketch. Since no annotations are provided, it carries full burden. It does not mention that the operation is non-destructive, but the sibling's name implies it.

    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 only two sentences, both front-loaded with the core purpose and usage. Every word adds value with no redundancy.

    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 simple getter tool with no parameters and no output schema, the description adequately explains the return format and content. It could optionally mention that it does not alter state, but this is implied by the sibling tool.

    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?

    The input schema has zero parameters, and schema description coverage is 100%, so no additional parameter information is needed. Baseline of 4 for zero parameters is appropriate.

    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 that the tool retrieves the current sketch in SVG format and lists drawable shapes, distinguishing it from the sibling tool 'clear_sketch' which clears the sketch.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly says 'Use this to see what the user drew,' providing clear context for usage. It does not specify when not to use it, but the sibling context is sufficient for differentiation.

    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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