Retrieve comprehensive AI-powered business intelligence for a company including industry, revenue, employee counts, and executive info. Premium API. Cost: $0.25 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| lookup | Yes | Company name, website, or email |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims it is a retrieval operation ('Retrieve'), which implies read-only and non-destructive behavior, but the annotations set destructiveHint=true, indicating the opposite. This is a direct contradiction. No other behavioral traits (e.g., rate limits, response format) are disclosed beyond the annotations.
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 with two sentences: one for purpose and one for cost. It is front-loaded, with no unnecessary words or repetition, effectively communicating the tool's value proposition.
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?
The description covers the main return fields (industry, revenue, employee counts, executive info) but lacks details on output format, any limitations (e.g., geographic scope, data freshness), and prerequisites. For a paid tool with a single parameter, the basics are present but depth is missing.
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% with the single parameter 'lookup' already documented as 'Company name, website, or email'. The description does not add any additional semantic detail about the parameter, so it meets the baseline without improvement.
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 verb 'Retrieve' and the resource 'comprehensive AI-powered business intelligence for a company', listing specific data types like industry, revenue, employee counts, and executive info. This distinguishes it from the many sibling tools that focus on more narrow domains such as address matching or company verification.
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 the many siblings, such as interzoid_company_verification or interzoid_executive_profile. The only usage clue is the cost mention, which may discourage casual use, but it fails to explicitly state when to choose this over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct data operations (matching, standardization, enrichment) with clear descriptions. Minor overlaps exist, e.g., address_match_advanced vs global_address_match, but descriptions differentiate them.
All tools follow a consistent 'interzoid_descriptive_function' pattern in snake_case, making it easy to predict purpose from the name.
58 tools is high for a single server, exceeding the typical 3-15 range. While each serves a specific data enrichment function, the quantity may overwhelm agents without clear categorization.
The tool surface covers a broad domain including address, company, person, and financial data. Minor gaps exist (e.g., no reverse IP lookup, limited social media coverage), but core data needs are well-addressed.
