get_figma_context

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It explains the process (fetch, extract, return), implies read-only analysis, and adds important caveats: user_id must be the account owner's UID and is NOT the study_id. This goes beyond the schema and provides meaningful behavioral context, though it does not explicitly state safety traits or potential side effects.

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

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

The description is front-loaded with a clear one-sentence purpose, followed by a concise process statement and then detailed but relevant parameter explanations. Every sentence earns its place; the note about user_id is essential and not redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has two parameters, no annotations, and an output schema. The description covers the core behavior, input semantics, and a key caveat. Given the output schema exists, detailed return values are not needed in the description. It is contextually complete, though it could mention any access prerequisites or error conditions, but the existing content is sufficient for an agent to use it correctly.

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

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—and it does excellently. Both user_id and figma_link are thoroughly explained, including the critical distinction that user_id is the account owner's UID, not the study_id, and advice to ask the user if unknown. This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Analyze a Figma prototype and extract screen context for UX research,' providing a specific verb (analyze) and resource (Figma prototype). It further clarifies that it fetches the file, extracts screen information, and returns structured data about pages and frames, which clearly distinguishes this from the sibling study/session management tools.

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

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

The description gives clear context for usage: it is for UX research on Figma prototypes, as stated in the first sentence and the process description. It does not explicitly mention alternatives or when not to use the tool, but the context is clear enough for an agent to select it appropriately.

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