Identify and categorize a company's primary industry classification with standardized NAICS and SIC codes, sector and subsector details, business activity description, and a confidence score. Premium API. Cost: $0.25 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| lookup | Yes | Company name to classify |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description implies a read-only query (identify/categorize), but annotations set destructiveHint=true, creating a contradiction. No explanation of the destructive behavior or whether it modifies data. Cost disclosure is helpful but insufficient to resolve the inconsistency.
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?
Two sentences, clear and front-loaded. No wasted words.
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 no output schema, the description lists expected outputs (NAICS, SIC, sector, etc.). However, the destructiveHint contradiction is unresolved, and no mention of error handling or rate limits.
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?
Single parameter 'lookup' has schema coverage 100%, and the description repeats the schema's description ('Company name to classify'). Does not add meaning beyond the schema.
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: 'Identify and categorize a company's primary industry classification with standardized NAICS and SIC codes, sector and subsector details, business activity description, and a confidence score.' This is specific and distinct from sibling tools (e.g., company matching, 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?
No guidance on when to use this versus sibling tools like interzoid_business_info or interzoid_company_verification. The only context is the cost and premium nature, but no usage scenarios or exclusions.
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.
