search_user_metrics

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

With no annotations, the description carries the full behavioral burden. It discloses key constraints beyond the schema: the date range may span at most 7 days, and end_date requires start_date because IT Glue rejects an open start. It also explains the nature of the returned data (counts, date-bucketed) and its relationship to reputation scores. It does not mention auth requirements or rate limits, but for a search tool this is sufficient.

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 three sentences with no fluff. The main action is front-loaded, the resource scope is explained immediately, and the critical constraints are stated last yet clearly. Every sentence contributes either to what the tool does, what the data represents, or how to constrain the call.

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 the 8-parameter schema, 100% parameter coverage, and absence of an output schema and annotations, the description does a strong job. It explains return data structure, date restrictions, and the tool's purpose. Minor gaps remain, such as the precise response shape and default behavior when no filters are applied, but the description is complete enough for safe invocation.

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 baseline is 3. The description adds value by linking parameters to the data model: per-user, per-organization, per-resource-type counts directly maps to user_id, organization_id, and resource_type. It also clarifies the relationship between start_date and end_date, which is not obvious from the schema alone. This pushes it above baseline.

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 opens with a specific verb and resource: "Search IT Glue user activity metrics." It then details exactly what is returned—per-user, per-organization, per-resource-type counts of actions bucketed by date—and adds context about being the raw data behind reputation scores. This clearly distinguishes it from sibling search tools that target organizations, configurations, locations, passwords, or documents.

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 provides clear context for when to use this tool: whenever user activity metrics are needed, and it explicitly frames the data as raw reputation-scores input. It does not name alternatives or exclusions, but the domain is so distinct from all sibling tools (which search entities, not metrics) that an agent can confidently select it. The lack of explicit 'when-not-to-use' guidance prevents a 5.

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