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Pruthvik1

figma-svg-to-icon-mcp

by Pruthvik1
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TypeScript
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Server Quality Checklist

67%
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  • Latest release: v1.0.1

  • Disambiguation5/5

    The two tools are clearly distinct: one extracts icons from Figma via URL, the other converts raw SVG code. No overlap in input methods, making selection unambiguous.

    Naming Consistency3/5

    Naming is mixed: 'extract_icons_from_figma' follows a verb-object-source pattern, while 'svg_to_icon' is a transformation phrase without a verb. Both use snake_case, but the semantic patterns differ.

    Tool Count3/5

    Only two tools exist, which is borderline per the rubric. However, the server's narrow purpose (Figma to icon conversion) is directly served by these two operations, so the count is reasonable.

    Completeness5/5

    The two tools cover the entire conversion pipeline: batch extraction from Figma and single conversion from raw SVG. Duplicate checking and file writing are included, leaving no obvious gaps for the stated purpose.

  • 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
    • 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?

    With no annotations, the description must disclose side effects. It mentions the write is optional, but critically omits the 'similarity check' noted in the icons_dir parameter, whether existing files are overwritten, and the behavior of dry_run. This leaves the AI uncertain about the tool's safety and failure modes.

    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 sentence that front-loads the core purpose and concisely notes the optional write behavior. There is no redundancy or filler; every word earns its place.

    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?

    The tool has no output schema and no annotations, but the description does not explain the return value (TSX code), the dry_run behavior, or the file write conditions (similarity check). These are significant gaps for a 4-parameter tool, leaving the AI without essential workflow details.

    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?

    All parameters have full schema descriptions, so the baseline is 3. The description adds little beyond the schema, only loosely tying the optional write to the icons_dir parameter without adding new meaning or usage nuance.

    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 uses the specific verb 'Convert' and clearly identifies both the input ('raw SVG code') and output ('TSX icon component'). This differentiates it from the sibling tool extract_icons_from_figma, which handles Figma extraction.

    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 context is clear: use this tool when you have raw SVG code that needs conversion to a TSX component. However, it does not explicitly mention alternatives or exclusion conditions, so it lacks the explicit guidance needed for 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 full burden and does disclose meaningful behavior: it uses the Figma REST API directly, handles deep/nested nodes and instances, checks the existing icon folder, and skips duplicates. It does not mention authentication or rate limits, but it covers the main behavioral traits.

    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 three sentences with each sentence serving a distinct purpose: purpose, behavior/method, and parameter-naming caveat. It is front-loaded and contains no filler.

    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 moderately complex tool, the description covers purpose, network method, nested-node handling, duplicate behavior, and a parameter caveat. It does not specify auth requirements or output details, but the input schema fully documents the four parameters, making the description reasonably complete.

    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?

    Schema description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by explicitly warning that the parameter name is figma_url and not figma_node_url, which helps avoid a common invocation error.

    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 is explicit: 'Extract all SVG icons from a Figma node URL and save new ones as TSX components.' It names the specific resource (Figma node URL), the action, and the output format, and the mention of using the Figma REST API clearly differentiates it from sibling svg_to_icon.

    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?

    It provides clear context for using the tool: extract from Figma node URLs, write TSX files, and handle duplicates. It also warns that the parameter must be figma_url, not figma_node_url. However, it does not explicitly name the sibling tool as an alternative or state when not to use it.

    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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