Cost Allocation

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

Annotations already indicate readOnlyHint=true, idempotentHint=true. The description adds value by stating that no prompts or responses are read, which is a privacy assurance beyond annotations. It also describes data sourcing (OpenTelemetry GenAI spans) and included outputs, enhancing transparency.

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 concise with four front-loaded sentences. Each sentence adds unique value: main purpose, data source, included outputs, and privacy note – no redundancy or fluff.

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 presence of an output schema and the tool's moderate complexity (4 optional params, LLM cost rollups), the description adequately covers the purpose, data source, and privacy. It does not detail the exact output structure, but the output schema handles 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% with detailed parameter descriptions. The tool description does not add additional semantics beyond what is already in the schema, so it meets the baseline of 3 without enhancement.

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 'Return' and the resource 'chargeback / showback LLM spend rollups', specifying the dimensions (cost-center, allocation tag) and included outputs (allocation, budget posture, forecast). It also distinguishes itself from sibling tools like cost_forecast and cost_report by focusing on rollups with budget and forecast.

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 for retrieving cost allocation data but does not explicitly compare to alternatives or state when not to use. It provides clear context on what the tool returns, but lacks explicit usage guidance or exclusions.

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