gram_schmidt

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Apply Gram-Schmidt orthogonalization') but does not describe what the tool returns (e.g., orthogonal vectors, orthogonal basis), error conditions, performance characteristics, or side effects. For a mathematical transformation tool with zero annotation coverage, this is a significant gap in transparency.

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 concise and front-loaded, consisting of a single sentence that directly states the tool's purpose. There is no wasted verbiage, and it efficiently communicates the core functionality. However, the inclusion of domain and category in parentheses, while informative, slightly disrupts the flow but does not significantly detract from clarity.

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 transformation tool with 2 parameters, 0% schema description coverage, no annotations, and no output schema, the description is incomplete. It lacks details on input format, output format, error handling, and mathematical assumptions (e.g., linear independence). For a tool that performs a non-trivial operation, more context is needed to ensure correct usage.

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%, meaning the schema provides no descriptions for the parameters. The description does not compensate by explaining what 'vectors' should contain (e.g., format, representation) or what 'normalize' does (e.g., whether it normalizes to unit vectors). This leaves the parameters largely undocumented, failing to add meaningful semantics beyond the bare 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 the tool's purpose: 'Apply Gram-Schmidt orthogonalization to a set of vectors.' It specifies the verb ('Apply Gram-Schmidt orthogonalization') and resource ('a set of vectors'), and includes domain/category context. However, it does not explicitly differentiate from sibling tools like 'orthogonalize' or 'normalize_vector', which could perform similar or related operations.

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 no guidance on when to use this tool versus alternatives. It mentions the domain and category but does not specify prerequisites, typical use cases, or when to choose this over sibling tools like 'orthogonalize' or 'normalize_vector'. Without such context, the agent must infer usage from the tool name and parameters alone.

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