Enrich UK Business

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 describes what data is returned and the lookup mechanism, but does not disclose behavioral traits such as rate limits, authentication needs, error handling, or whether it's a read-only or mutative operation. The description adds some context (e.g., 'from a single lookup') but misses key operational details that would help an agent use it correctly.

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 appropriately sized and front-loaded, starting with the core purpose and then detailing the returned data and parameters. Every sentence earns its place by adding necessary information without redundancy, making it efficient and easy to parse for an AI agent.

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 (a tool that aggregates multiple data sources), lack of annotations, and no output schema, the description is incomplete. It covers the purpose and parameters well but does not describe the return format, error conditions, or other behavioral aspects needed for full contextual understanding. This leaves gaps that could hinder correct tool invocation by an agent.

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 schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds value by explaining the optional parameters ('Optionally include a Companies House number or website domain for more precise results') and providing high-level context for their use, but does not add significant semantic details beyond what the schema provides. With high coverage, the baseline is 3, and the description slightly enhances this with usage hints.

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 with a specific verb ('Get') and resource ('comprehensive profile of any UK business'), detailing what data is returned (Companies House data, Google Places ratings, website/SSL status, social media links) and how it's obtained ('from a single lookup'). It distinguishes itself by aggregating multiple data sources into one operation, which is explicit even without sibling tools.

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 when to use this tool by stating it's for getting a comprehensive business profile from multiple sources, but it does not provide explicit guidance on when to use it versus alternatives (e.g., other lookup tools) or any exclusions. Since no sibling tools are listed, the lack of comparative guidance is less critical, but it still lacks explicit usage context beyond the basic purpose.

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