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

With no annotations provided, the description takes full responsibility for behavioral disclosure. It explicitly states that the tool 'operates deterministically with zero side effects,' which is crucial for agent decision-making. This exceeds minimal requirements.

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 consists of two sentences, with the first sentence immediately stating the core functionality. There is no redundant information, and each sentence adds value. Efficient and well-structured.

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 (kinematics, robotics) and the absence of an output schema, the description should ideally hint at the return structure or output content. It mentions the computed quantities but not how they are returned, leaving some information gaps for the 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?

The input schema covers both parameters with 100% description coverage. The description adds context about the parameters (e.g., 'mass matrices' implies usage of robot_definition) but does not significantly deepen understanding beyond the schema. A baseline score of 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 that the tool calculates mass matrices, Christoffel symbols, and Jacobian derivatives, with specific use cases like real-time trajectory tracking and path planning. It distinguishes itself from unrelated sibling tools (fuzz_boundaries, quantize_tensors) but does not explicitly differentiate them.

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 mentions applications like real-time tracking and path planning validation, implying suitable contexts. However, it does not explicitly state when not to use the tool or provide alternative tools, leaving usage guidance somewhat implicit.

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