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Outline a text or topic: make a structured Markdown outline of a text or topic

ai_outline
Read-only

A structured Markdown outline (headings and bullet points) of a text you send, or a suggested outline for a topic. Use to plan articles, reports, talks and documentation. Example: text=How small businesses can accept stablecoin payments. Price $0.01/call via x402 USDC or a card key (/credits), first 25 free; failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input Schema

NameRequiredDescriptionDefault
textYesText to outline, or a topic, up to 12,000 characters
depthNoHeading levels, 1-3 (default 2)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and destructiveHint=false, yet the description still adds real context: pricing model, free-tier terms, and that failed calls are free. 'Each call is charged, so repeats are not free' usefully reinforces the idempotentHint=false annotation. It doesn't describe latency or output length limits, so not a 5.

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 core purpose and output format are front-loaded in the first sentence, followed by usage and an example. The pricing/key sentence is dense but earns its place by warning the agent that calls are billed and non-idempotent.

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?

For a two-parameter generation tool with no output schema, the description covers what comes back (Markdown headings and bullets), what goes in, and the cost model. Only the depth parameter's effect on output shape is left unexplained.

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?

Schema coverage is 100%, so both parameters are already fully documented. The description's example clarifies what the 'text' value looks like in practice but adds nothing about the 'depth' parameter (1-3 heading levels), leaving it to the schema. Baseline 3 is appropriate.

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

Purpose4/5

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

States a specific verb and output artifact ('structured Markdown outline of headings and bullet points') plus the two input modes: a supplied text or a topic. It does not explicitly differentiate from near-siblings like ai_summarize_text or ai_rewrite, so it stops short of a 5.

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

Usage Guidelines4/5

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

Gives concrete use cases ('plan articles, reports, talks and documentation') and a worked example, which is clear usage context. It never says when not to use it or names an alternative tool for overlapping tasks such as summarizing.

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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