search

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions searching docs and guidance but doesn't describe key behaviors like whether results are paginated, if there are rate limits, authentication requirements, or what the output format looks like (e.g., text snippets, links). This leaves significant gaps for an agent to understand how the tool operates beyond basic purpose.

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 a single, efficient sentence that front-loads the purpose and usage without unnecessary words. It directly states what the tool does and why to use it, making it easy to parse and understand quickly, with no wasted information.

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 lack of annotations and output schema, the description is incomplete for a search tool. It doesn't explain what the tool returns (e.g., search results format, error handling) or behavioral constraints, which are critical for an agent to use it effectively. The description covers basic purpose but misses important operational details needed for full context.

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 description coverage is 100%, with the single parameter 'query' fully documented in the schema as a 'Natural language query describing the information you need about Markdownify.' The description doesn't add any additional parameter semantics beyond what the schema provides, so it meets the baseline for high schema coverage without compensating with extra details.

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 clearly states the tool searches 'Markdownify connector docs and previously shared guidance' with the purpose of learning 'how to use this server inside ChatGPT or via API.' It specifies the resource (documentation/guidance) and verb (search/learn), though it doesn't explicitly differentiate from sibling tools like 'bing-search-to-markdown' or 'webpage-to-markdown' which search external content rather than internal docs.

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 description implies usage context ('to learn how to use this server') but doesn't explicitly state when to use this tool versus alternatives like browsing docs manually or using other search tools. It suggests a learning purpose but lacks clear exclusions or comparisons to sibling tools, leaving some ambiguity about optimal use cases.

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