Generate an AI-powered similarity key for product name matching. Handles variations in product names, model numbers, and descriptions. Cost: $0.01 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| product | Yes | Product name, description, or model | |
| algorithm | No | Algorithm variant (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses cost ($0.01 USDC via x402), adding transparency beyond annotations. However, does not explicitly mention destructive nature (already indicated by destructiveHint=true) or what the similarity key is.
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: first states purpose, second adds cost and capabilities. Efficient and front-loaded with no waste.
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?
Covers core functionality and cost, but lacks output format details (no output schema). Fine for a simple tool, but could be more complete.
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?
Description reinforces schema for 'product' parameter (variations) but adds nothing for 'algorithm'. Schema coverage is 100%, so baseline is 3; description adds marginal value.
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?
Clearly states the tool generates a similarity key for product name matching, handling variations. Distinct from sibling tools which focus on addresses, companies, etc.
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 explicit when-to-use or alternatives mentioned. Context implies use for product name matching, but lacks guidance on prerequisites or when not to use.
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.
