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Rewrite for Answer Engine Optimization

rewrite_aeo
Read-only

Rewrite content to optimize for AI answer engines like ChatGPT, Perplexity, and Google AI Overviews. Add BLUF opening, FAQ structure, and schema markup based on the target query.

Instructions

Rewrite a content block for Answer Engine Optimization. Adds a BLUF opening, FAQ structure, schema additions, and concise question-shaped headings tuned for ChatGPT / Perplexity / Google AI Overviews.

Read-only when given url (one HTTP GET). Zero network when given text. The tool does NOT write back to the URL - it only returns the rewritten content as a string. No side effects on the source.

This tool delegates the actual rewrite to the calling LLM via MCP sampling - it does not call any external API itself. The MCP host's model produces the rewrite. Same input may produce different output across runs (model-dependent).

When to use: optimizing content for direct-answer surfaces (definitions, how-tos, FAQs). For Generative Engine Optimization (entity-rich, comparison-ready synthesis), use rewrite_geo instead.

Either url or text must be provided. target_query is required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoPublic URL whose content should be fetched and rewritten. Either this OR `text` is required.
textNoRaw content (markdown or HTML) to rewrite directly. Either this OR `url` is required.
formatNoOutput shape. `article` for prose-with-headings. `faq` for Q&A list. `howto` for numbered-step procedural content with HowTo schema hints. `comparison` for X-vs-Y tables. Default `article`.article
max_wordsNoSoft word budget for the rewrite. Default 1500. Range 100-5000. The rewrite tries to stay under this; very small budgets may force truncation.
target_queryYesThe user query the rewrite should answer (e.g. `what is RAG`, `how to deploy Ghost to Docker`). Required - drives heading shape and BLUF wording.
respect_robotsNoIf true (default), respect robots.txt when fetching `url`. Ignored when `text` is used.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
notesYesNotes from the rewrite (e.g. truncations, format adjustments).
formatNoOutput format (article, faq, howto, comparison).
sourceYes
rewrittenYesThe rewritten content. The caller decides where to publish it.
target_queryYes
Behavior5/5

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

The description fully explains behavior beyond annotations: read-only when given url (one HTTP GET), zero network when given text, no side effects, delegates to LLM via sampling (no external API), and acknowledges non-idempotence due to model variability. No contradictions with annotations.

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

Conciseness5/5

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

The description is concise (8 sentences), well-structured, and front-loaded. Each sentence adds value: purpose, behavior, delegation mechanism, usage guidance, and input requirements. No fluff.

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

Completeness5/5

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

Given the tool's complexity (6 params, delegation to LLM, output schema exists), the description covers all essential aspects: purpose, behavior, parameter requirements, usage alternatives. The presence of an output schema obviates the need to describe return values.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed descriptions. The description adds value by clarifying the mutual exclusivity of `url`/`text` (not enforced in schema), listing `format` enum examples, and explaining `target_query` drives heading shape and BLUF wording.

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 title and description clearly state the tool rewrites content for Answer Engine Optimization, listing specific transformations (BLUF, FAQ, schema, headings) and target AI overviews. It distinguishes from sibling `rewrite_geo` by stating use cases.

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 explicitly says when to use (direct-answer surfaces) and contrasts with `rewrite_geo`. It also specifies that either `url` or `text` must be provided, and `target_query` is required, providing clear practical guidance.

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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