Standardize city name data to a consistent canonical form. Handles abbreviations, alternate spellings, and local variations. Cost: $0.01 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| city | Yes | City name to standardize | |
| state | No | State or province (optional but recommended) | |
| algorithm | No | Algorithm variant (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate destructiveHint=true, but the description portrays the tool as a harmless standardization process without mentioning side effects. The cost info adds transparency, but the contradiction between description and annotations reduces clarity.
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 very concise: two sentences front-loading purpose and key features. Every word adds value, with no redundancy.
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?
Given the simplicity of the tool and the presence of annotations, the description covers basic functionality and cost but omits return format and potential side effects hinted by annotations.
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 no additional meaning beyond what the schema provides, merely restating the purpose.
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 with a specific verb 'standardize' and resource 'city name data'. It details handling of abbreviations, alternate spellings, and local variations, making the functionality distinct from siblings like interzoid_state_abbreviation or interzoid_country_standard.
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 implies use for city name standardization but does not explicitly guide when to use this tool over siblings. No exclusions or alternatives are mentioned, relying on the tool name for context.
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.
