evidence_get_sum

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

With no annotations provided, the description must carry the behavioral disclosure burden. It communicates a read-only aggregation operation and the existence of optional filters, but does not describe how filters interact, error behavior, or whether the sum is server-computed. This is adequate but thin.

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 front-loaded sentence with no redundant wording. Every part contributes to defining the operation and its optional filtering behavior.

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 that an output schema exists and all parameters are documented, the description provides the minimal core. Still, it does not clarify when to choose this tool over siblings, how 'conditions' and 'filter_expr' combine, or what kind of totals are returned, leaving moderate gaps for an agent.

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 parameters are already documented in the schema. The description adds only the general notion of optional filtering, not specific parameter semantics, so the baseline of 3 is appropriate.

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 a specific verb ('Get'), resource ('evidence'), and operation type ('summation/totals'). This distinguishes it from sibling tools like evidence_get and evidence_get_record_changes, which return records rather than aggregates.

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 phrase 'optionally filtered' implies this tool is for aggregated totals rather than raw records, so usage context is inferable. However, it does not explicitly name alternatives or state when this tool should be preferred over evidence_get or other evidence getters.

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