orthogonalize

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

No annotations are provided, so the description carries the full burden. It mentions the operation but lacks behavioral details such as what algorithm is used (e.g., Gram-Schmidt), whether it modifies inputs or returns a new vector, error handling for linearly dependent vectors, or performance characteristics. This leaves significant gaps for a mutation-like operation.

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 extremely concise—a single sentence with no wasted words. It is front-loaded with the core purpose and includes domain/category context efficiently, making it easy to parse despite its brevity.

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 complexity of a linear algebra operation with 2 parameters, 0% schema coverage, no annotations, and no output schema, the description is insufficient. It lacks details on behavior, parameters, return values, and error conditions, making it incomplete for reliable tool invocation.

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 0%, and the description provides no parameter details. It doesn't explain what 'vector' and 'basis_vectors' represent (e.g., numeric arrays, format expectations), their dimensions, or constraints. For a tool with 2 required parameters and no schema descriptions, this is inadequate.

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 action ('Orthogonalize') and the resource ('a vector with respect to a set of vectors'), with a specific domain and category. It distinguishes from siblings by focusing on orthogonalization, which is not directly covered by other tools like 'normalize_vector' or 'vectors_orthogonal'. However, it doesn't explicitly differentiate from potential similar linear algebra operations beyond the domain hint.

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 explicit guidance on when to use this tool versus alternatives is provided. The domain and category hints imply usage in linear algebra contexts, but there's no mention of prerequisites, when not to use it, or how it compares to sibling tools like 'gram_schmidt' or 'vector_projection' that might handle related operations.

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