pku_search_datasets

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

With no annotations, the description carries the full burden. It discloses the return fields (title, DOI, release date, source dataverse, landing page URL) and the scope (497 published datasets), which goes beyond the schema. It does not mention pagination behavior, but the schema covers start/per_page, and it does not describe side effects (none expected for a read-only search). The disclosure of concrete output fields is valuable and 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 two sentences with zero redundancy. The primary purpose is front-loaded, and the return fields are listed compactly. Every word earns its place, making it efficient for an agent to parse.

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 simple search tool with 100% schema coverage and no output schema, the description covers the essential information: what it searches, the scope, and the return fields. It does not explicitly explain how to page through results, but the schema provides start/per_page. It also does not mention sorting or default behavior, but those are minor gaps for a straightforward search. Overall, it is complete enough for correct invocation.

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 the parameters are already well-documented in the input schema. The description does not add any extra meaning beyond what the schema provides (e.g., query is the search term, start is offset, per_page is limit). It neither clarifies nor expands on the parameters, so a baseline of 3 is appropriate.

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 states a specific verb ('Full-text search') and resource ('datasets') with a precise scope ('497 published datasets of Peking University Open Research Data Platform'). It clearly distinguishes itself from sibling tools like pku_search_files (search files) and pku_get_dataset (retrieve a specific dataset), leaving no ambiguity about what this tool does.

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 clearly implies the use case: searching across datasets. It does not explicitly mention alternatives or exclusions, but the verb 'search' and the resource 'datasets' make it obvious this is the tool for dataset search, not file search or dataset retrieval. The context is clear enough for an agent to select correctly without extra guidance.

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