validate_number_in_base

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 states the tool validates a number string in a base, implying a read-only check with no side effects, but does not disclose behavioral traits like error handling (e.g., invalid inputs), return format (e.g., boolean or detailed message), or performance considerations. For a validation 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 extremely concise—a single sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose, and the domain/category annotation is efficiently appended. Every part of the description earns its place, making it highly efficient.

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 (validation with two parameters), lack of annotations, and no output schema, the description is incomplete. It does not cover behavioral aspects, parameter details, or return values, leaving the agent with insufficient information to use the tool effectively beyond a basic understanding of its purpose.

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 has 0% description coverage, so the description must compensate. It mentions 'number string' and 'given base', aligning with the parameters 'number' (string) and 'base' (integer), but adds minimal semantic context. It does not explain expected formats (e.g., alphanumeric for bases >10), valid ranges for 'base', or examples, leaving parameters largely undocumented.

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: 'Validate if a number string is valid in the given base.' It specifies the verb ('validate'), resource ('number string'), and context ('in the given base'), making the intent unambiguous. However, it does not explicitly differentiate from sibling tools like 'is_valid_base' or 'base_conversion', which handle related but distinct 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 lacks context on prerequisites, typical use cases, or comparisons with siblings such as 'is_valid_base' (which might check base validity) or 'base_conversion' (which might handle conversions). Without this, the agent must infer usage from the tool name alone.

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