get_knowledge_query_metrics

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by specifying that the tool returns aggregated counts and can be scoped to a project or entire organization, providing behavioral context beyond what annotations convey. No contradictions.

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 consists of two sentences that are front-loaded and free of extraneous information. Every sentence adds value: the first identifies the tool's action and resource, the second specifies the return type and scope. Ideal conciseness.

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 no output schema, the description adequately explains the return type (aggregated counts) and scope (project/org). It covers the key aspects of the tool. However, it could implicitly link the date parameters to the aggregation, but the schema descriptions handle that.

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%, so baseline is 3. The description does not add additional parameter semantics beyond what the schema already provides (e.g., projectId description 'Omit for organization-wide metrics' is already in schema). It marginally reinforces the scope but does not introduce new meaning.

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 the verb 'Gets', the resource 'Cognigy.AI Knowledge AI query metrics', and specifies the output as 'aggregated knowledge search/RAG query counts'. It also distinguishes scope (project or organization), differentiating it from sibling metrics tools like get_conversation_metrics.

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 usage context (Knowledge AI query metrics) but provides no explicit guidance on when to use this tool versus alternatives such as get_conversation_metrics or get_call_metrics. No 'when-to-use' or 'when-not-to-use' statements are present.

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