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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds behavioral context beyond that by specifying the content scope (data studies, playbooks, field notes on agentic commerce) and the return fields (titles, summaries, URLs). While it doesn't discuss pagination or limits, for a simple read-only search this is reasonably transparent.

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?

The description is exactly two sentences and front-loaded with the action ('Search...'). It packs essential information—resource, content type, return values, and usage trigger—without any filler or redundant phrasing. Every clause earns its place.

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 tool with a single required parameter, read-only annotations, and no output schema, the description is sufficiently complete. It tells the agent what the tool does, what it returns, what kind of topics are covered, and when to use it. The lack of pagination or result-limit details is not a significant gap for a straightforward search tool.

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 schema covers the single 'query' parameter with a description and examples, achieving 100% schema coverage. The tool description complements this by enriching the semantic context—e.g., listing specific topics like 'AI attribution', 'MCP', 'AI catalog accuracy', and 'ChatGPT ads'—which helps the agent formulate better queries. This is more than just a repetition of schema field names.

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?

The description opens with a specific verb ('Search') and a clearly identified resource ('SearchShopAI's Research Notes blog'), and further defines scope with content types and example topics. It also states the return payload (titles, summaries, URLs), which gives a concrete sense of what the tool does. This strongly distinguishes it from sibling tools, especially get_research_note, by signaling search over retrieval.

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?

An explicit usage context is provided: 'Use when asked what SearchShopAI has written or published about a topic.' This gives the agent a clear trigger scenario. However, it does not explicitly mention when not to use it or point to an alternative tool (e.g., get_research_note for fetching a specific note), so it stops short of a full exclusion/alternative comparison.

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