scf_get_evidence_assessment_summary

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

No annotations are provided, so the description carries the full burden. It only lists the returned metrics without disclosing behavioral traits such as read-only nature, required permissions, potential side effects, or performance considerations. For a read operation, basic transparency is lacking.

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 key information. Every word contributes to the purpose and outputs, with no unnecessary elaboration.

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 is simple with one parameter and no output schema. The description adequately lists the returned metrics for an aggregate summary. It could mention that results are read-only or dashboard-level, but overall it covers the essential context.

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 100%, and the parameter org_id is already well-documented with a clear description and format. The description adds no additional meaning beyond the schema, so baseline score 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 it retrieves aggregate AI assessment metrics for the organization dashboard, listing specific outputs like total assessed, counts by status, unassessed count, average relevance score, and total cost. This differentiates it from sibling tools such as scf_get_evidence_assessment (individual) or scf_get_evidence_validation_summary (validation-focused).

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 use for dashboard-level overview but does not explicitly state when to use this tool versus alternatives like scf_get_evidence_assessment or scf_get_window_assessment_summary. No when-not or alternative guidance is provided.

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