formula_kinetic_laws

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

With no annotations provided, the description carries the full burden. It discloses the return structure (a JSON object with kinetic_laws array and fields like reaction_id, math, parameters) and an example, which is significant. It does not address error handling or edge cases, but for a read-only retrieval tool, this is adequate.

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 with a summary, args, returns, and example. The Returns block is detailed but directly useful for understanding the output. It is slightly longer than necessary but every section serves a 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?

With a single parameter and a full example of the return structure, the description provides everything an agent needs to invoke the tool correctly. The output schema existence is supported by the detailed Returns example, making the description complete for this simple tool.

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?

The schema provides only the parameter name 'model_id' with no description (0% coverage). The description fully compensates by specifying 'BioModels 模型 ID（如 "BIOMD0000000012"）' with a concrete example format, adding essential meaning beyond the schema.

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 a specific verb+resource: '獲取 BioModels 模型的動力學公式' (retrieve kinetic laws from BioModels models) and elaborates with '從 SBML 模型中提取所有動力學方程式' (extract all kinetic equations from SBML models). This distinguishes it from sibling tools like formula_get or derivation tools by focusing on SBML kinetic laws.

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 clear context for when to use the tool: to get kinetic laws from a BioModels model. It does not explicitly mention alternatives or exclusions, but the purpose is unambiguous. The example call reinforces the usage pattern, so it earns a 4 rather than 3.

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