Return the canonical llm.txt summary for AI agents in one request so LLMs can get the design system's key details without multiple lookups.
Return llm.txt — the canonical single-fetch summary for AI agents.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral load. 'Return' correctly implies a read-only, side-effect-free operation, and 'single-fetch' hints that one call yields the whole payload, but it says nothing about response size, format, or caching behavior. Adequate but thin for a no-annotation tool.
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?
A single front-loaded sentence with no filler; the resource and its purpose are stated immediately with nothing wasted.
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?
For a zero-parameter, zero-annotation tool with no output schema, the description is serviceable but not complete: an agent cannot tell what sections llm.txt actually contains or how it relates to the sibling read_* documents. Given the tool's simplicity the gap is modest, but it leaves routing decisions to inference.
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?
The tool takes zero parameters, so the baseline is 4; the schema trivially covers 100% of the (empty) parameter set and there is nothing for the description to compensate for. No parameter ambiguity exists.
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 (Return) plus a specific resource (llm.txt) and characterizes its role as the 'canonical single-fetch summary for AI agents', which differentiates it from the many sibling read_* tools that fetch individual docs. An agent can tell what this returns without opening anything else.
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 phrase 'single-fetch summary for AI agents' implies this is the consolidated entry point, so the usage is inferable, but the description never says when to prefer this over read_readme, read_agents, or the other read_* siblings. No explicit when/when-not guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.