Estimate API response cost for a specific LLM model
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model ID (gpt-4, claude-3.5-sonnet, etc) | |
| content | Yes | API response content | |
| input_tokens | No | Optional input token count |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the full burden of disclosing behavioral traits. It states the core action ('estimate') but does not clarify whether this is a read-only calculation, what outputs are returned, any required permissions, or potential side effects. The behavior is under-disclosed for an agent to fully anticipate the tool's 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?
The description is a single, well-structured sentence that immediately conveys the tool's purpose. It contains no filler or redundant information, earning the highest score for conciseness and front-loading.
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?
The tool is relatively simple with 3 parameters, all fully described in the schema. Although there is no output schema or annotations, the description makes the return value implicit ('cost') and the invocation clear. It is nearly complete for this level of complexity, though it could be improved by explicitly stating the output format.
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 schema already documents all parameters clearly. The description adds no additional meaning beyond the schema, such as suggesting how 'input_tokens' interacts with 'content' or providing examples. Baseline 3 is appropriate since the schema does the heavy lifting.
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 action ('estimate'), the resource ('API response cost'), and the specific scope ('for a specific LLM model'). This distinguishes it from sibling tools like compare_models, which likely handles comparisons, and analyze_response, which likely focuses on content quality.
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?
No guidance is provided on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or references to sibling tools. The phrase 'for a specific LLM model' slightly implies a single-model context, but there is no explicit when-to-use or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool targets a distinct aspect of cost analysis: token counting, cost estimation, model comparison, and optimization. The descriptions clearly differentiate them, so an agent would not confuse one for another.
All tool names follow a consistent verb_noun pattern with snake_case (analyze_response, compare_models, estimate_cost, optimize_payload). This is predictable and easy to navigate.
Four tools is well-scoped for a focused analyzer. Each tool covers a core function without unnecessary overlap or bloat, making the set feel complete yet manageable.
The tool set covers the full lifecycle of response cost analysis: analyze content, estimate cost, compare models, and optimize. There are no obvious missing operations for this domain.