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

With no annotations, the description carries the full burden. It discloses batch limits, resolution behavior, and fuzzy matching with suggestions. However, it does not explicitly state if the operation is read-only or side-effect-free, which would improve transparency further.

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 consists of two focused sentences: the first states the core function and constraints, the second provides crucial usage guidance. Every word earns its place, and the structure is front-loaded with the most important information.

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?

Despite the simple schema, the description lacks details about the output format. It mentions 'canonical company refs' and 'suggested close matches' but does not specify the structure (e.g., object fields like id, name). Given no output schema, this omission reduces completeness for an agent that needs to chain results.

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 coverage is 100%, so the description adds value by specifying that each query can be a ticker, exact/partial company name, or company ID. This enriches the agent's understanding beyond the schema's generic 'string' type.

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 that the tool batch-resolves up to 25 strings (tickers, company names, or IDs) to canonical company refs. This distinguishes it from sibling tools like search_companies or get_company, which serve different purposes.

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 explicitly directs the AI agent to call this tool first when processing raw user input, and it explains that unresolved entries receive suggestions. This provides clear when-to-use guidance and implies when not to use (when IDs are already known).

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