sumif_records

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

With no annotations, the description carries full burden. It states the core behavior (conditional aggregation) and lists operators, but lacks details on read-only nature, error handling, performance, or dependencies on record structure.

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, no fluff, front-loaded with purpose and analogy. Every word adds value.

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 7 parameters and no output schema, the description provides the core concept and operators. It omits details like the need for records to contain the fields, but the schema descriptions cover the rest. Overall sufficient for a straightforward tool.

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 86%, so baseline is 3. The description adds the Excel analogy and operator list, which helps contextualize parameters, but does not significantly exceed what the schema already provides.

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 it is a conditional aggregation tool analogous to Excel SUMIF/COUNTIF/AVERAGEIF, specifying the verb (sum, count, average) and resource (records field). It distinguishes itself from siblings like aggregate_records by focusing on conditional logic.

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 provides a clear mental model via the Excel analogy, implying usage for conditional aggregations. However, it does not explicitly contrast with siblings like aggregate_records or group_by_aggregate, nor does it state when not to use it.

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