cost_projection

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

The description adds beyond schema by mentioning the credit cost and output structure (totals per model, ranking). With no annotations provided, this is good disclosure. However, it does not cover rate limits or potential side effects.

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?

Two sentences convey the core function and a key behavioral detail (credit cost). No redundant or unnecessary information. Front-loaded with the main purpose.

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?

For a tool with no output schema, the description adequately explains the output shape and includes important context like credit cost. It covers the 4 parameters well via schema. Minor gaps include not mentioning error conditions or prerequisites, but overall complete for its complexity.

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 the schema already documents all parameters. The description adds context about credit cost and output, but does not add significant detail beyond what the schema's descriptions provide. Baseline 3 is appropriate.

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 tool projects cost for token-usage workloads across 1-10 models and returns specific outputs (daily/weekly/monthly/yearly totals and ranking). It distinguishes itself from siblings like compare_models and get_model_pricing by focusing on cost projection across multiple models.

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 clearly indicates when to use the tool (projecting costs for multiple models), but does not explicitly state when not to use it or provide alternatives among sibling tools. It's clear but lacks exclusionary guidance.

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