Rewrite for Generative Engine Optimization

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

Annotations (readOnlyHint true, openWorldHint true) are consistent. Description adds critical behavioral details: does not write back to source URL, returns string, delegates rewrite to the calling LLM via MCP sampling (no external API call), and output may vary across runs. No contradictions.

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?

Concise and well-structured: first sentence captures core purpose, then expands on behavior, usage guidelines, alternative tool, and parameter hints. Every sentence adds value with no redundant content.

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?

Given the complexity (6 parameters, 1 required, output schema exists) and richness of annotations, the description covers all necessary aspects: purpose, usage, behavioral transparency, parameter semantics, and alternatives. The existence of an output schema makes return value explanation unnecessary.

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 coverage is 100%, so baseline is 3. Description adds value by explaining the role of `target_query` in driving entity selection and comparison framing, clarifying the `add_comparison_table` parameter for X-vs-Y queries, and noting mutual exclusivity of `url` and `text`. This exceeds the baseline.

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?

Clearly states the tool rewrites a content block for Generative Engine Optimization with specific characteristics (entity-rich, comparison-ready, synthesis-friendly). Explicitly distinguishes from sibling tool `rewrite_aeo` by describing different use cases.

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?

Provides explicit guidance on when to use (optimizing for synthesis-style answers across multiple sources) and when not to (use `rewrite_aeo` for direct-answer optimization). Also clarifies read-only behavior and delegation to LLM via sampling.

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