Skip to main content
Glama
nvanexan

@nvanexan/figma-mcp

by nvanexan
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one extracts design context as CSS-like properties, while the other exports rendered images. There is no overlap in functionality, making it easy for an agent to choose the right tool based on the need for data extraction versus visual output.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'get_figma_' as a prefix, using snake_case throughout. This predictable naming scheme enhances readability and makes the tool set coherent and easy to understand.

    Tool Count2/5

    With only two tools, the server feels thin for a Figma integration, which typically involves more operations like creating or updating designs, listing files, or managing comments. The limited scope may restrict agent workflows, suggesting an incomplete surface for the domain.

    Completeness2/5

    The tool set is severely incomplete for a Figma MCP server, covering only context extraction and image export. It lacks core operations such as file management, design creation, or collaboration features, which are essential for comprehensive Figma integration, leading to potential agent failures in broader tasks.

  • Average 3.3/5 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 is failing

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 provided, the description carries the full burden of behavioral disclosure. It mentions exporting an image as a URL, implying a read operation, but doesn't cover aspects like authentication needs, rate limits, error handling, or what the URL format entails (e.g., temporary vs. permanent). This leaves significant gaps for a tool that interacts with an external service.

    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, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action, making it easy to parse and understand quickly.

    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 moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks details on behavioral traits and usage context, which are important for an external API tool. The high schema coverage helps, but overall completeness is limited.

    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 has 100% description coverage, detailing each parameter's purpose, constraints, and defaults. The description adds no additional semantic context beyond what the schema provides, such as examples or edge cases, so it meets the baseline for high schema coverage.

    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 ('Export') and resource ('a rendered image of a Figma node'), making the purpose understandable. However, it doesn't differentiate from the sibling tool 'get_figma_context', which might handle different aspects of Figma data, so it doesn't reach the highest score.

    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 like 'get_figma_context' or other image export methods. It lacks context about prerequisites or exclusions, leaving the agent to infer usage from the parameters alone.

    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?

    With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions what gets extracted (layout, styles, text, dimensions), it doesn't address important behavioral aspects like authentication requirements, rate limits, error conditions, or what happens with invalid URLs. The description provides some context but leaves significant behavioral questions unanswered.

    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, efficient sentence that front-loads the core purpose. Every word earns its place - 'Extract' (action), 'design context' (what), 'from a Figma URL' (source), 'as CSS-like properties' (output format). No wasted words or redundant 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?

    For a tool with no annotations and no output schema, the description provides basic purpose but lacks important contextual information. It doesn't explain what the output looks like (beyond 'CSS-like properties'), doesn't mention authentication or rate limiting, and doesn't provide examples of what 'design context' includes beyond the parenthetical list. The 100% schema coverage helps, but the description itself is incomplete for a tool that extracts complex design data.

    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?

    With 100% schema description coverage, the input schema already fully documents both parameters. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it mentions 'Figma URL' and 'depth' concepts but provides no additional context about URL formats beyond what the schema specifies or how depth affects the extraction.

    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 specific action ('Extract'), target resource ('design context from a Figma URL'), and output format ('CSS-like properties'). It distinguishes from the sibling tool 'get_figma_image' by focusing on contextual data extraction rather than image generation.

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

    Usage Guidelines3/5

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

    The description implies usage through the phrase 'from a Figma URL' but doesn't explicitly state when to use this tool versus alternatives. No guidance is provided about when not to use it or what specific scenarios it's designed for beyond the general extraction purpose.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

figma-mcp MCP server

Copy to your README.md:

Score Badge

figma-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nvanexan/figma-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server