scf_get_evidence_assessment_summary

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

Annotations already declare readOnlyHint=true, so the safe read-only nature is covered. The description adds useful context about the scope (organization dashboard) and the exact metrics returned, but it does not disclose potential nuances like how statuses are categorized, whether aggregates are point-in-time or time-bounded, or how average relevance is computed. Given the annotation coverage, a middle score is appropriate.

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 well-structured sentence that front-loads the core purpose and then compactly lists the returned metrics. Every word earns its place, with no filler or 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?

For a simple read-only tool with one well-documented parameter, the description provides enough context for an agent to select and invoke it correctly. It names the data returned, the aggregation scope, and the cost unit. The only minor gap is the absence of any note about response shape or empty-result behavior, but the output schema is absent and the tool is simple enough that this is not a significant omission.

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?

There is only one parameter, org_id, and its schema description is 100% covered, even providing a source hint ('obtain from scf_list_organizations'). The tool description adds no parameter-level meaning beyond the schema, but none is needed given the high schema coverage.

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 states a specific verb ('Get') and resource ('aggregate AI assessment metrics') and enumerates the exact returned fields (total assessed, counts by status, unassessed count, average relevance score, total cost in cents). It clearly identifies what the tool does, though it does not explicitly differentiate it from sibling summary tools like scf_get_evidence_validation_summary or scf_get_window_assessment_summary.

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 'for the organization dashboard' implies the intended use case: retrieving high-level aggregate assessment metrics for display or reporting. However, there is no explicit guidance on when to prefer this tool over sibling summary tools, nor any exclusions or alternative recommendations.

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