get_llm_unit_economics_full

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 explains the computation logic (fetching spend and dividing by metrics) and lists outputs. It implicitly indicates a read-only operation. However, it does not address failure scenarios (e.g., missing provider configuration) or specify the response format.

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 well-structured: a summary line, bullet points of outputs, a parameter list, and example queries. It is front-loaded with the key purpose. A few redundancies exist (e.g., examples restate parameter purposes), but overall efficient for the amount of information conveyed.

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 lack of annotations and output schema, the description covers purpose, inputs, and computation comprehensively. It lacks details on the return structure and error scenarios, but for a tool with 5 optional parameters and no output schema, the coverage is strong.

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?

With 0% schema description coverage, the description compensates by listing all parameters (customers, mau, mrr, api_requests, days) with brief explanations and example usage. This adds significant meaning beyond the schema's type/default info. Could be improved with units or constraints.

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 'Fetches AI spend across all configured providers and divides by your business metrics' and lists specific computed metrics. The tool name includes 'full', differentiating it from the sibling 'get_llm_unit_economics' by promising a more comprehensive breakdown including cross-provider project/workspace analysis.

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 four example queries that illustrate when to use the tool (e.g., asking for cost per customer, cost per API request). However, it does not explicitly contrast with the sibling tool 'get_llm_unit_economics' or state conditions where the simpler version should be used instead.

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