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JayZeeDesign

figma-mcp

by JayZeeDesign
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.1

  • Disambiguation5/5

    Each tool targets a distinct Figma element: components, nodes, and workflows. There is no overlap in their purposes, making it easy for an agent to select the correct tool.

    Naming Consistency5/5

    All tools follow a consistent 'get_' + singular noun pattern (get_components, get_node, get_workflow), which is predictable and easy to follow.

    Tool Count5/5

    With 3 tools, the server is tightly scoped for retrieving key Figma file elements. This number is appropriate for a focused MCP server without being too sparse or excessive.

    Completeness4/5

    The tools cover retrieval of components, nodes, and workflows, which are important aspects of a Figma file. However, it lacks tools for pages or styles, which are common use cases, so minor gaps exist.

  • Average 3.7/5 across 3 of 3 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.

  • 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.

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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 burden for behavioral disclosure. It states the tool 'gets' components (read-only) and returns a list, but lacks details on authentication requirements, error behavior, pagination, or limits. For a single-file retrieval, this is minimal.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is efficient, using a structured Args/Returns format without fluff. It front-loads the purpose in the first line. Minor redundancy: the Args section repeats schema info, but overall it's concise.

    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 simple read tool with one parameter and no output schema, the description covers the essential: purpose, parameter meaning, and return type. However, it doesn't define 'components' or mention any limitations, making it adequate but not thorough.

    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 0%, so the description must compensate. It explains 'file_key' as 'The file key found in the shared Figma URL', which adds meaningful context beyond the schema's bare title 'File Key'. This is clear and helpful.

    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 action ('Get components') and the resource ('in a Figma file'). It distinguishes from sibling tools like get_node (which retrieves a single node) and get_workflow (which retrieves a workflow) by focusing on listing components.

    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 this tool versus siblings. No context about prerequisites, typical scenarios, or when not to use it. The description only states what it does, not when to choose it.

    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?

    The description does not disclose behavioral traits beyond the basic purpose. Since no annotations are present, it should state that the operation is read-only or any potential side effects, but it lacks such transparency.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise and well-structured with a main sentence, Args, and Returns sections. It contains no unnecessary words, though the return type is informally specified.

    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 adequately covers the input and return type but lacks details about error conditions and the internal structure of the returned workflow connections. Given the simplicity of the tool, it is minimally complete but could be improved.

    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 description explains the file_key parameter with a concrete example from a Figma URL, adding significant meaning beyond the input schema which only provides the parameter name. This compensates for the 0% schema description coverage.

    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 it gets workflows available in a Figma file, specifying the verb (get) and resource (workflows). It distinguishes from siblings like get_components and get_node by targeting a different resource type.

    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 is provided on when to use this tool versus alternatives. It only describes the function without offering context on appropriate usage scenarios or exclusions.

    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?

    Describes return behavior (node data or empty dict if not found) and parameter format. No annotations provided, so description carries burden. Misses rate limits or auth needs but is sufficient for a simple get.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Well-structured with Args and Returns sections, but the example URL is overly long. Could be more concise without losing clarity.

    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?

    Covers both parameters and return value. No output schema, but description explains return behavior. Slightly lacking in error cases other than not found, but adequate for a simple retrieval.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema coverage, description fully compensates by explaining file_key with an example and node_id format requirement. Adds significant meaning beyond schema titles.

    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?

    Clearly states 'Get a specific node from a Figma file' with a specific verb and resource. Distinguishes from sibling tools like get_components and get_workflow.

    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?

    Implies usage by describing the action, but does not explicitly guide when to use this tool versus alternatives. No when-not or exclusion criteria.

    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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