list_organisms

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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses important behaviors: empty query returns the first max_results organisms, max_results is capped at 100, and filtering is a case-insensitive substring match on code or name. This adds real operational context.

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 well-structured and front-loaded: a one-sentence purpose, a useful context note about list size, and a compact Args list. Every sentence adds value without redundancy.

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 tool's moderate complexity, the annotations, and the output schema, the description covers everything an agent needs: what is returned, how to filter, pagination/default behavior, and constraints. No critical gaps remain.

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 0%, but the description fully compensates with an Args section explaining what each parameter does: query is a case-insensitive substring filter, max_results limits and caps the result count, and defaults are clarified. This is significantly more informative than the bare schema.

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-resource pair: 'List organisms available in KEGG with their 3-4 letter codes and names.' This clearly distinguishes the tool from the sibling search_* and get_* tools, which target specific entities rather than enumerating organisms.

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 gives clear practical guidance: the list is large, so pass a query to filter by code or name rather than paging through everything. It does not explicitly name alternative tools or say when not to use this tool, but the context is unambiguous enough for an agent to choose it appropriately.

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