minimize_function

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

With no annotations, the description carries the full burden of behavioral disclosure. It does not mention the algorithm used, convergence criteria, limitations, or error handling. Agents are left unaware of potential issues like non-convergence or local vs. global minima.

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 two short sentences and a parameter list. Every sentence adds value, and the structure is front-loaded with the core 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?

Given the tool's complexity (mathematical optimization) and the lack of output schema or annotations, the description is somewhat minimal. It covers purpose and parameters adequately but omits return values, potential errors, and algorithmic details, which could be important for an AI agent.

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?

Although schema coverage is 0%, the description adds meaning beyond types: 'expression' is a mathematical expression in variable 'x', and 'guess' is the starting point with a default of 0. This clarifies key details not evident from the schema alone.

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 finds the local minimum of a univariate function near an initial guess. It uses a specific verb ('求' meaning 'find') and resource ('局部极小值'), which distinguishes it from siblings like find_root or integrate_function.

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. For instance, it does not explain that this tool is for minimization, while find_root is for root-finding. There are no explicit when-to-use or when-not-to-use instructions.

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