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

With no annotations provided, the description fully bears the transparency burden. It discloses return fields (tissue, cancer type, total mutations, etc.), ordering (by mutation count), scope limitation, and that it returns multiple matching entries. This gives the agent a complete behavioral model without needing to infer.

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?

Three sentences, front-loaded with the core action, then returns and usage guidance. Every sentence adds value—no filler, no repetition of schema details.

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 tool has no output schema, so the description must convey what is returned. It enumerates all result fields, data sources, ordering, and scope, making it self-sufficient for correct invocation and interpretation of results. The usage guidance also positions it within the sibling tool family.

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 descriptions already cover 100% of parameters, including query matching rules and limit bounds. The description adds 'ranked by mutation burden' as context for the query but does not add new semantics beyond schema; it reinforces the ordering concept without introducing new parameter 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 starts with a specific verb 'Find' targeting 'cancer cell lines by name, ranked by mutation burden', clearly distinguishing it from sibling tools like search_genes (genes) and get_cell_line (specific entity).

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?

Explicitly states 'Use before `get_cell_line` or `top_genes_in_cell_line`', providing clear context on when to use it relative to alternatives. Also clarifies scope ('only cell-line models, kept separate from primary tissue samples') and ordering, which implicitly guides when not to use.

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