Textile Designer AI guide

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful behavioral context beyond that: answers are grounded in the website's own documentation, and results can legitimately differ from the original. It does not state answer format or any limits, but the bar is lower given the annotations.

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?

Front-loaded with the core purpose, and every parenthetical clause maps a topic value to its meaning, so nothing is wasted. It is one long run-on sentence, which is dense but not padded.

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?

With only two parameters, 100% schema coverage, and no output schema, the description covers the essential decision surface: what the tool answers and which topic selects which content. It could say more about the shape of the returned guidance, but nothing critical for invocation is missing.

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 100%, so the baseline is 3, but the description earns above that by explaining what each topic value actually returns (choose_tool = tool fit, workflows = typical chains, compare = how tools differ, etc.). It also clarifies the optional tool param's role for compare and models.

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?

States a specific verb and resource ('Answers how-to questions from the website's own documentation') and enumerates exactly what topics it covers, from tool selection to credits. This clearly separates it from the sibling processing tools, which perform actions rather than answer questions.

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 parenthetical mapping routes the agent to the right topic value for each kind of question (choose_tool for tool fit, workflows for chains, compare for differences, etc.), which is strong usage guidance. It stops short of saying when NOT to use this tool (e.g. vs describe_tool/list_tools) or that tool is only needed for compare/models.

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