get_knowledge_rows

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

No annotations are provided, so the description carries full burden. It discloses that it returns 'rows with their full rowData, plus count and totalCount for the list', which adds behavioral context beyond the input schema. However, it lacks details on permissions, rate limits, error handling, or whether it's read-only/safe (though 'fetch' implies read). For a tool with no annotations, this is minimal but not misleading.

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?

Two sentences with zero waste: first states purpose and usage, second specifies return values. It's front-loaded with the core action and context, and every sentence adds value (e.g., clarifying output structure). Efficient and well-structured.

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 2 parameters with full schema coverage, no annotations, and no output schema, the description is adequate but has gaps. It explains the purpose and output ('rows with full rowData, count, totalCount'), which helps compensate for missing output schema. However, for a data-fetching tool with no annotations, it could better address safety, permissions, or data freshness to be more complete.

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%, so the schema fully documents both parameters (listKey and limit). The description adds no parameter-specific semantics beyond what's in the schema (e.g., it doesn't elaborate on listKey examples beyond 'investors, deals' or explain limit implications). Baseline 3 is appropriate as the schema does the heavy lifting.

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 action ('Fetch sample rows') and resource ('from a knowledge list'), and specifies the purpose is to 'inspect actual data' with 'example payloads from investor/deal lists'. It distinguishes from sibling tools like 'get_knowledge_text' by focusing on row data rather than text content. However, it doesn't explicitly differentiate from 'list_knowledge_lists' which might list metadata vs. actual row data.

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 ('Use this to inspect actual data — see example payloads'), suggesting it's for data exploration rather than operational tasks. It doesn't provide explicit when-not-to-use guidance or name alternatives among siblings (e.g., 'get_knowledge_text' for text content or 'list_knowledge_lists' for metadata). The guidance is helpful but not comprehensive.

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