Figma MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: adding files, posting comments, reading comments, replying to comments, and viewing nodes. There is no overlap in functionality, making it easy for an agent to select the correct tool without confusion.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., add_figma_file, post_comment, read_comments, reply_to_comment, view_node). This uniformity enhances readability and predictability for agents.
Tool Count5/5
With 5 tools, the server is well-scoped for its purpose of managing Figma files and comments. Each tool serves a specific, necessary function without bloat or redundancy.
Completeness4/5
The toolset covers core operations for file management and comment interactions, but lacks CRUD coverage for files (e.g., no update or delete file tools). However, agents can likely work around this minor gap for basic workflows.
Average 2.8/5 across 5 of 5 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
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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 provided, so the description carries the full burden of behavioral disclosure. 'Add a Figma file to your context' implies a mutation or storage action, but it doesn't clarify if this requires specific permissions, what 'context' refers to (e.g., workspace, session), whether the action is reversible, or any rate limits. The description is too vague to inform the agent adequately about 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, straightforward sentence that is front-loaded with the core action. It avoids unnecessary words and gets directly to the point. However, it could be more structured by including key details like the tool's scope or constraints, but as-is, it's efficiently concise.
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?
Given the lack of annotations and output schema, the description is incomplete for a tool that likely performs a mutation (adding a file). It doesn't explain what 'context' means, the result of the operation, or any error conditions. For a tool with one parameter but no structured behavioral hints, the description should provide more context to be fully helpful.
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, with the 'url' parameter clearly documented as 'The URL of the Figma file to add'. The description adds no additional meaning beyond this, such as URL format requirements or validation rules. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter documentation effectively.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('Add') and resource ('Figma file'), but it's vague about what 'add to your context' means operationally. It doesn't distinguish from sibling tools like 'view_node' or 'post_comment', which might involve similar resources. The purpose is understandable but lacks specificity about the tool's exact function.
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 doesn't mention prerequisites, such as needing a valid Figma URL or authentication, or how it differs from sibling tools like 'view_node' that might also interact with Figma files. The description offers no context for selection among available tools.
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 the full burden of behavioral disclosure. It mentions the action ('Post a comment') but fails to describe critical behaviors such as whether this requires specific permissions, if comments are publicly visible or private, rate limits, error handling, or what happens on success (e.g., comment ID returned). For a mutation tool with zero annotation coverage, this is a significant gap.
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 purpose without unnecessary words. It is appropriately sized and front-loaded, with every word earning 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?
Given the complexity of a mutation tool (posting comments) with no annotations and no output schema, the description is incomplete. It lacks information on behavioral traits (e.g., permissions, visibility), usage context, and expected outcomes. The high schema coverage helps with parameters, but overall context is insufficient for effective tool selection and invocation.
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 documents all 5 parameters thoroughly. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't explain parameter interactions or provide examples). According to the rules, baseline is 3 when schema coverage is high (>80%) and no param info is in the description.
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 ('Post a comment') and the target ('on a node in a Figma file'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'reply_to_comment' or 'read_comments', which would require explicit comparison to achieve a score of 5.
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 'reply_to_comment' or 'read_comments', nor does it mention prerequisites such as file access permissions or comment visibility. It only states what the tool does without contextual usage instructions.
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 the full burden of behavioral disclosure. While 'Get all comments' implies a read operation, it doesn't specify whether this requires authentication, rate limits, pagination behavior, or what format the comments are returned in. This leaves significant gaps for a tool that presumably interacts with an external API.
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 with zero wasted words. It's appropriately sized for a simple tool and front-loads the essential information.
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?
For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'all comments' means in practice (e.g., format, structure, limitations), nor does it address authentication requirements or error conditions that would be important for an API interaction tool.
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 description mentions 'on a Figma file' which aligns with the single 'file_key' parameter, but adds no additional semantic context beyond what the schema already provides (100% coverage). The baseline score of 3 is appropriate since the schema fully documents the parameter.
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 ('Get all comments') and target resource ('on a Figma file'), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'view_node' or 'post_comment' that might also involve Figma file interactions, so it doesn't reach the highest clarity level.
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 'post_comment' or 'reply_to_comment'. There's no mention of prerequisites, context for retrieving comments, or any explicit when/when-not instructions.
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?
No annotations are provided, so the description carries full burden. It states the action ('Reply to') but lacks behavioral details: it doesn't disclose required permissions, whether this is a write operation, rate limits, response format, or error conditions. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.
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 with zero waste. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place, and there's no redundancy or unnecessary elaboration.
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?
Given the tool's complexity (a mutation with 3 required parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain return values, error handling, or behavioral traits, leaving the agent with insufficient context to use the tool effectively beyond basic purpose.
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 parameters are fully documented in the schema. The description adds no additional meaning beyond what the schema provides (e.g., no context on comment_id format beyond schema's '<number>', no examples). Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate or enhance parameter understanding.
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 ('Reply to') and target resource ('an existing comment in a Figma file'), making the purpose immediately understandable. It distinguishes from sibling tools like 'post_comment' (new comment) and 'read_comments' (viewing), though not explicitly. However, it doesn't specify the verb+resource combination as precisely as it could (e.g., 'post a reply' vs. 'reply to').
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. The description doesn't mention prerequisites (e.g., needing an existing comment), exclusions, or comparisons with siblings like 'post_comment' for new comments or 'read_comments' for viewing. Usage is implied but not explicitly stated, leaving gaps for an agent to infer context.
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 the full burden of behavioral disclosure. It states the tool 'Get a thumbnail,' implying a read-only operation, but doesn't clarify aspects like whether it requires authentication, has rate limits, returns image data or a URL, or handles errors. This leaves significant gaps in understanding the tool's behavior beyond its basic function.
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, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse. Every part of the sentence contributes directly to understanding the tool's function.
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?
Given the lack of annotations and output schema, the description is incomplete for a tool that retrieves visual data. It doesn't explain what a 'thumbnail' entails (e.g., image format, size, or how it's returned), which is critical for an AI agent to use the tool effectively. The schema covers inputs well, but the output behavior remains undocumented.
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, clearly documenting both parameters (file_key and node_id) with their purposes and formats. The description adds no additional semantic information beyond what the schema provides, such as examples or edge cases. This meets the baseline score since the schema adequately covers parameter details.
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 ('Get a thumbnail') and the target resource ('a specific node in a Figma file'), making the purpose understandable. However, it doesn't differentiate this tool from potential sibling tools that might also retrieve node information or thumbnails, as the sibling list includes file management and comment tools but no direct alternatives for viewing nodes.
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. It doesn't mention any prerequisites, such as needing the file key and node ID, or specify scenarios where this tool is appropriate over other methods for accessing node data. This lack of contextual direction limits its utility for an AI agent.
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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