Parse a full address string into component parts: street number, street name, unit, city, state, zip code. Cost: $0.01 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Full address string to parse |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states 'Parse' which implies a read-only operation, but annotations set destructiveHint=true, indicating a potential side effect or mutation. This contradiction undermines transparency. The description adds cost info but fails to clarify the nature of the operation.
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 concise sentences: one for purpose, one for cost. No extraneous information, efficiently front-loaded.
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 lists output components, partially compensating for missing output schema. However, it lacks details on output format, error handling, or when to use the tool. Given low complexity, it is fairly 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?
Schema description coverage is 100%, so the parameter 'address' is already described. The component list in the description is output-oriented and does not add meaning to the input parameter beyond what the schema provides.
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 specifies 'Parse a full address string into component parts' with a clear verb and resource, listing the parts (street number, street name, unit, city, state, zip code). It is distinct from sibling tools like 'interzoid_address_and_fullname_match'.
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 is provided on when to use this tool versus other address-related siblings. The description only mentions cost, not context for selection.
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.
