Figma MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
list_frames enumerates frame metadata while fetch_frame retrieves a single frame's content and writes artifacts. The clear singular/plural object distinction and verb difference make tool selection unambiguous.
Naming Consistency5/5Both tools follow the same verb_noun snake_case pattern: list_frames and fetch_frame. This is consistent and predictable.
Tool Count3/5Two tools form a minimal but coherent list-then-fetch pipeline. The count feels thin for a general Figma integration, but it is not absurd for a narrow frame-extraction server.
Completeness2/5The server only handles top-level frames and fetching one frame as a PNG/node.json artifact. It lacks support for nested frames, pages, components, design tokens, or any mutation, so broader Figma workflows quickly dead-end.
Average 3.5/5 across 2 of 2 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 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 passing
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.
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.jsonto 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
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint=true is directly contradicted by the description's statement that the tool 'transactionally write[s] frame.png, node.json, and ... fills.json' under the output directory. While the description usefully discloses the file write behavior, the existence of the files, and the preservation of unrelated files, the contradiction forces a score of 1 per the rubric. The annotations also present a conflicting pair (readOnlyHint true + destructiveHint true), increasing confusion.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and every clause contributes: the first sentence states the core action and file outputs; the second lists return proofs and the preservation guarantee. Information is front-loaded and there is no redundant wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description compensates by listing return proofs (upstream request proof, local size, SHA-256, PNG structure/dimensions, JSON read-back proof). It also notes that unrelated files are preserved, which signals the risk to the three named files. Gaps remain (e.g., no error behavior, no mention of whether out_dir is created, no guidance on obtaining node_id), but the core invocation context is covered well enough for a 4.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 references the output directory but does not add extra semantic detail for file_key, node_id, scale, or out_dir beyond what the schema already provides. No compensation is needed, so 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies a concrete action: read one Figma frame and write three named files (frame.png, node.json, fills.json) with normalized data. It clearly identifies the resource (a single frame) and expected outputs. However, it does not explicitly differentiate from the sibling list_frames, so it stops short of a full 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus list_frames, nor any prerequisites such as obtaining a node_id via list_frames or ensuring the output directory exists. There are no exclusions or alternative routing hints, leaving the agent to infer usage from the name.
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?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the description correctly does not restate safety. It adds genuinely useful context by naming the required file_content:read permission and the concrete return payload, including nullable dimensions, metadata, completeness, request ID, and rate-limit proof. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no fluff: the core action is front-loaded, followed by a compact list of return fields and the required permission. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter read-only list tool, the description covers the action, return values, and authentication requirement. The absence of an output schema is partially mitigated by enumerating return categories, though details on formatting or pagination are omitted.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both query and file_key are already sufficiently documented in the input schema. The description mentions filtering at a high level but adds no syntax or behavioral details beyond what the schema provides, so it stays at the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('List'), a clear resource ('top-level FRAME nodes in a Figma file'), and the optional filtering behavior. It effectively distinguishes from the sibling fetch_frame by focusing on all top-level frames rather than a single frame.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes it clear this tool is meant for enumerating or filtering top-level frames, which implies when it should be used. However, it never names the sibling fetch_frame or states when to prefer that alternative, so the usage guidance remains implicit rather than explicit.
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
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/scalably-io/figma-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server