eval_tool_call_accuracy

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

No annotations provided; description carries burden. It discloses deterministic behavior (no LLM), input parameters (expected/actual), and return format (score, passed, reason). Does not mention side effects, which is acceptable for a stateless evaluation tool.

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?

Description is succinct (~8 lines) with clear sections: purpose, behavior, args, returns. No wasted words; structure facilitates quick scanning.

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 tool's simplicity and presence of output schema, the description fully covers inputs, deterministic behavior, and return value. No gaps for an agent to use it correctly.

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 0%, but description adds meaning via Args section: explains each parameter (expected_tool, actual_tool, etc.) and notes optionality of arguments. Provides clarity beyond schema's type-only definitions.

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?

Description clearly states the tool evaluates tool call accuracy by comparing actual vs expected tool names and arguments. It distinguishes itself from sibling eval tools by focusing on tool call correctness and emphasizing deterministic nature without LLM judge.

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?

Description implies usage for objective evaluation but lacks explicit guidance on when to use this tool versus other eval tools like eval_answer_accuracy or eval_faithfulness. No when-not recommendations are given.

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