matrix_inverse

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

With no annotations, the description discloses error behavior for singular/non-square matrices, which is valuable. However, it fails to mention other traits like immutability, precision, or performance implications.

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 very concise—two sentences with no wasted words. It front-loads the core operation and adds critical error information efficiently.

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?

Despite having an output schema, the description does not mention that the tool returns the inverse matrix. For a simple tool, this is a minor gap; overall it is adequate but not fully complete.

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%, yet the description does not add meaning to the 'matrix' parameter beyond its type (already visible in schema). No constraints like real-valued or size requirements are provided.

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 computes the inverse of a square matrix, distinguishing it from sibling tools like determinant and solve_linear_system. It also mentions error handling for singular or non-square matrices, adding precision.

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 implies usage for computing inverses but offers no explicit guidance on when to use this vs alternatives like solve_linear_system. It lacks when-not or exclusion criteria.

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