xor_basis

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that the operation is over GF(2), but says nothing about the returned representation (list of basis vectors? rank?), determinism, or canonical form, so an agent cannot predict the output shape.

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?

A single front-loaded sentence with zero filler; the core operation is stated immediately and nothing is redundant.

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?

For a one-parameter pure-function tool this is minimally adequate, but with no output schema the description should explain what the computed basis looks like (e.g., a reduced basis array and/or its rank), which it omits.

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 description coverage is 100% for the single 'values' parameter ('Array of non-negative integers'), so the schema already documents the input. The description only restates 'a set of integers' and adds no boundary details (e.g., value range limits), making the baseline 3 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 gives a specific verb ('Compute') and resource ('a linear basis over GF(2) (XOR basis) for a set of integers'), which is precise enough that an agent knows exactly what mathematical operation this performs. It does not, however, differentiate itself from adjacent linear-algebra siblings like gaussian_elimination, matrix_rank, or linear_solve.

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?

There is no statement of when to use this tool versus alternatives, nor any prerequisites or constraints (e.g., integer width limits, whether the basis is reduced/canonical). The agent must infer the use case (XOR/linear-basis problems) entirely from the name and one sentence.

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