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

Although the readOnlyHint annotation already signals safety, the description adds significant behavioral context: it explains live lookup semantics, how email/domain/name searches map to contact/company records, how found=false should be interpreted, and what the lifecyclestage field conveys. It also states the response includes CRM properties and a URL to open each record, which is useful behavior beyond the annotation.

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 dense but every sentence earns its place: purpose, query interpretation, examples, return expectations, special field semantics, not-found behavior, and sibling tool routing. It is front-loaded with the core purpose and uses examples efficiently. Nothing is redundant or filler.

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?

For a lookup tool with readOnlyHint and 100% schema coverage, the description is complete: it defines query semantics, edge cases, field meaning, and what the response includes. There is no output schema, so the description's coverage of return values and found=false behavior is especially valuable. An agent has everything needed to call this tool correctly and interpret its result.

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 coverage is 100%, so the baseline is 3. The description adds meaningful interpretation of the query parameter by explaining how emails, domains, and names each resolve to contacts and companies. It adds semantic meaning beyond the schema's brief parameter descriptions, though portal_id and object_type remain well-covered by the schema itself.

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 uses a specific verb ('look up'), names the resource (person or company in the user's connected HubSpot CRM), and clarifies the live, searchable inputs: email, domain, or name. It gives concrete example queries and distinguishes itself from the aggregate sibling tools ask_about_hubspot_contacts and ask_about_hubspot_companies. An agent can immediately understand what this tool is for and how it differs from similar 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 explicitly provides example invocation scenarios ('what do I have on...', 'is gusto.com in the CRM', 'is Acme a customer') and directly states when to prefer the alternatives: 'For aggregate questions over many records use ask_about_hubspot_contacts or ask_about_hubspot_companies instead.' This gives an agent clear selection criteria without relying on inference.

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