Generate an advanced AI-powered similarity key for US street address matching. Handles unit numbers, directionals, and abbreviations. Cost: $0.01 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | No | Zip code (optional but recommended for precision) | |
| address | Yes | Street address | |
| algorithm | No | Algorithm variant (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include destructiveHint=true and readOnlyHint=false. The description adds cost transparency ($0.01 USDC) and confirms the tool generates a key without modifying input, which supplements the destructive annotation without contradiction.
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 two concise sentences, front-loading the primary purpose and adding cost info in the second. Every word serves a purpose 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?
With no output schema, the description does not specify the return format or practical use of the similarity key. It adequately covers parameter behavior but misses output details, leaving the agent partially uninformed.
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%. The description adds context beyond schema by explaining the tool handles unit numbers, directionals, and abbreviations, and that it produces an AI-powered similarity key, enhancing understanding of how parameters contribute to the output.
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 function: generating an advanced AI-powered similarity key for US street address matching. It specifies handling of unit numbers, directionals, and abbreviations, and distinguishes it from siblings like interzoid_address_parse and interzoid_global_address_match by focusing on US addresses and advanced matching.
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 usage for US address matching requiring fuzzy logic, but lacks explicit guidance on when to use this tool versus siblings or when not to use it. No alternatives or exclusions are mentioned.
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.
