Search by Data Type

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

With no annotations, the description fails to disclose behavioral traits such as whether the operation is read-only, pagination behavior, or error handling. It only states the basic function, missing details that help an agent anticipate side effects or constraints.

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, clear sentence with no filler. Every word adds value, and the core information is presented upfront, making it easy for an agent to parse quickly.

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?

The description is adequate given the output schema (which documents return values) and low parameter count. However, it lacks details on pagination, valid entity types, and does not reference sibling tools to guide selection, leaving some gaps.

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 has 0% description coverage, but the description adds meaning to 'entity_type' by listing example values (samples, sequences, biodata, taxa). However, 'limit' is not explained, and the description does not clarify that entity_type may be an enumeration or free text, leaving ambiguity.

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's purpose: to find specific types of biological or environmental data, listing examples like samples, sequences, biodata, and taxa. This distinguishes it from siblings like search_by_name or search_by_source, which filter by other criteria.

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 the tool is for searching by type but provides no explicit guidance on when to use it versus alternatives (e.g., advanced_query, bbox_search). It does not mention when not to use it or specify any prerequisites, leaving the agent to infer usage from context.

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