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alucardeht

Figma MCP

by alucardeht

get_full_page_context

Retrieve complete Figma page data including sections, screenshots, assets, and design tokens in a single API call for efficient multi-agent implementation workflows.

Instructions

Get complete page context in ONE call with all sections, assets, screenshots, and styles.

WHAT YOU GET IN ONE CALL:

  • Complete page structure with all sections identified

  • Screenshots for each section (base64 encoded)

  • All assets organized by section with unique names

  • Design tokens per section

  • Asset map for quick lookup

  • Agent instructions ready for parallel implementation

  • Transition elements that span multiple sections

PERFECT FOR:

  • Getting full context before implementation

  • Preparing data for parallel multi-agent work

  • Quick assessment of page complexity

  • One-call solution for complete page understanding

RETURNS:

  • overview: Frame metadata and recommendations

  • sections: Array with all section details including screenshots

  • assetMap: Quick lookup table for assets by unique name

  • agentInstructions: Pre-written instructions for each agent

  • transitionElements: Elements spanning multiple sections

TYPICAL WORKFLOW:

  1. get_full_page_context → get everything at once

  2. Distribute sections to multiple agents using agentInstructions

  3. Each agent implements their section with all necessary context

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_keyYesFigma file key from URL
page_nameYesPage name (partial match)
frame_nameYesFrame name (partial match)
scaleNoScreenshot scale 1-4 (default: 2)
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 of behavioral disclosure. It effectively describes key behaviors: it's a read operation (implied by 'Get'), returns comprehensive data (listed in 'WHAT YOU GET IN ONE CALL' and 'RETURNS'), and supports parallel implementation workflows. However, it lacks details on error handling, rate limits, or authentication needs, which are important for a tool with multiple parameters and no output schema.

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 well-structured with clear sections (WHAT YOU GET, PERFECT FOR, RETURNS, TYPICAL WORKFLOW), making it easy to scan. It's appropriately sized for a complex tool, though some redundancy exists (e.g., listing items in both 'WHAT YOU GET' and 'RETURNS'). Most sentences earn their place by adding value, but minor trimming could improve conciseness.

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?

Given the tool's complexity (4 parameters, no annotations, no output schema), the description does a good job of explaining what the tool does, when to use it, and what it returns. It covers purpose, usage guidelines, and behavioral context comprehensively. However, the lack of an output schema means the description must fully explain return values, which it does in the 'RETURNS' section, though some details (e.g., data formats for screenshots) could be more explicit.

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, providing clear documentation for all 4 parameters (file_key, page_name, frame_name, scale). The description adds no additional parameter-specific information beyond what's in the schema. According to the rules, when schema_description_coverage is high (>80%), the baseline score is 3 even with no param info in the description, which applies here.

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 explicitly states the tool 'Get complete page context in ONE call with all sections, assets, screenshots, and styles.' It uses specific verbs ('Get complete page context') and resources ('sections, assets, screenshots, and styles'), clearly distinguishing it from siblings like get_screenshot (single screenshot) or extract_assets (assets only). The 'ONE call' emphasis highlights its comprehensive nature versus piecemeal alternatives.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance with sections like 'PERFECT FOR:' and 'TYPICAL WORKFLOW:'. It specifies when to use ('Getting full context before implementation', 'Preparing data for parallel multi-agent work') and implies alternatives by contrasting its 'ONE call' approach with sibling tools that handle specific aspects (e.g., extract_assets, get_screenshot). The workflow steps outline a clear sequence, reinforcing its role in multi-agent scenarios.

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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