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agmonetti

plan-export-mcp

by agmonetti
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TypeScript
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools target different output types and inputs: one exports whole Markdown plans/audits, the other renders isolated Mermaid diagrams. There is mild potential for confusion when a plan includes diagrams, but the descriptions make the boundary clear.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern: export_plan and render_diagram. The naming is parallel, predictable, and accurately reflects each tool's function.

    Tool Count3/5

    With only two tools, the server feels thin but is still reasonably scoped for a narrow plan-export and diagram-rendering purpose. It is not bloated, yet it sits at the lower boundary of acceptable tool count.

    Completeness4/5

    The core workflows are covered: exporting plans to common document formats and rendering diagrams to image formats. Minor gaps exist, such as batch processing or combining plan text and diagrams into a single export, but agents can accomplish the primary tasks without dead ends.

  • Average 4/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 45 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

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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

  • Behavior3/5

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

    With no annotations, the description carries the disclosure burden. It conveys the core behavior—converting Markdown to styled PDF/PNG/HTML—but does not mention that files will be written to an output directory, possible overwrite behavior, or that outputName/outputDir defaults apply.

    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, information-dense sentence with no filler. It front-loads the input type and then enumerates output formats and styling, so every clause earns its place.

    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 tool is simple and the schema covers all parameters, but with no annotations and no output schema the description still leaves gaps: no mention of file-system side effects, no guidance on default output naming, and no cue about when render_diagram would be the better choice.

    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%, so the input schema already explains all five parameters including defaults. The description adds only context about styling ('matching IDE aesthetics'), not new parameter-level meaning, keeping this at the baseline.

    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 names a specific verb ('Export'), resource ('AI agent implementation plan or audit (Markdown)'), and target formats (PDF, PNG, HTML), so an agent knows what the tool produces. It does not explicitly contrast with render_diagram, but the Markdown-plan/audit focus is enough to avoid confusion for a typical call.

    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 gives clear context for when to use the tool: whenever an implementation plan or audit in Markdown needs to be exported to a styled document format. It does not state exclusions or explicitly name the sibling as an alternative, which prevents a 5.

    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?

    With no annotations provided, the description carries the behavioral disclosure burden. It reveals that the tool writes an isolated file, crops the output, and can include a base64 visual preview in chat. It could mention overwrite behavior or whether rendering requires external services, but 'isolated' plus direct-to-file and preview details give a useful safety picture.

    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 sentence front-loads the action and resource, then efficiently enumerates diagram types, output format, file target, and chat preview. There is no filler or redundant restatement of the tool name.

    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 six-parameter tool without an output schema, the description covers the essential workflow: Mermaid input, cropped PNG/SVG file output, and optional visual preview. The schema handles defaults and enums; the description could be more explicit about exactly what the tool returns to the caller, but an agent has enough to invoke it correctly.

    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%, so the baseline is 3 and the description is not required to document every parameter. It adds the supported Mermaid diagram types, which the schema does not list, but otherwise mostly paraphrases the format and includeBase64 parameters rather than adding substantial new parameter-level meaning.

    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 names a specific verb and resource: render a Mermaid diagram, enumerates supported diagram types, and specifies the output format (cropped PNG/SVG) plus optional chat preview. This clearly distinguishes it from the sibling export_plan, which concerns plans rather than rendering diagram definitions into image files.

    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 gives clear context for when to use the tool: converting Mermaid definitions into standalone cropped image files or providing an in-chat visual preview. It does not explicitly state when not to use it or name the sibling as an alternative, but the supported diagram types and output modes make the intended scope clear.

    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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