get_org_topology

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

The description notes the tool is 'Computed from raw measurements,' which indicates an aggregating, read-only computation consistent with the readOnlyHint annotation. It adds value beyond the annotation by describing provenance and the analytical nature of the result, though it does not address things like auth or rate limits.

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, information-dense sentence that front-loads the core concept and then lists specific value-adding capabilities. There is no filler or redundant phrasing.

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?

With zero parameters, an output schema, and a readOnly annotation, the description is largely complete for selecting and invoking the tool correctly. It thoroughly describes the content of the topology map, though it could briefly mention what kinds of questions or scenarios it is best used for.

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?

The tool has zero parameters, and the baseline for such tools is 4. There is no parameter ambiguity to resolve, so the description does not need to compensate for missing parameter documentation.

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 names a specific resource ('Organization-level AI topology map') and enumerates the exact analytical dimensions it provides, such as team-level metric distributions and Gini coefficient. This distinguishes it clearly from sibling tools like get_operator_profile and get_cohort_distribution.

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?

Usage context is implied through the organization-level scope and the list of team/capability metrics, but the description does not explicitly state when to prefer this tool over alternatives or mention any exclusions. It does not name sibling alternatives despite a large sibling list.

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