Retrieve property transaction history for any US address including last sale date and price, prior transactions over up to ten years, price-per-square-foot trajectory, ownership chain, recorded deed types, mortgage history, and current owner of record. Premium API. Cost: $0.25 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | US property street address |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description contradicts the annotations: it describes a read-only operation ('Retrieve property transaction history'), yet annotations set destructiveHint=true and readOnlyHint=false. Additionally, while it mentions cost and payment method ($0.25 USDC via x402), the contradiction undermines trust and 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 a single paragraph that lists many data points, making it somewhat lengthy. While it includes important cost information, it could be more concise. It is adequately structured but not exceptionally efficient.
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 the contradiction, the description provides a comprehensive list of returned data (last sale, prior transactions, etc.), which compensates for the lack of an output schema. The tool has only one parameter, so the description is nearly complete for an agent to understand what to expect.
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?
The only parameter 'address' is described as 'US property street address' in the schema, and the description does not add further semantic detail. With schema coverage at 100%, the description provides no additional value beyond the schema, meeting the baseline.
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 retrieves property transaction history for a US address, listing specific data points such as last sale date, price, and ownership chain. The verb 'retrieve' and resource 'property transaction history' are unambiguous. Among the long list of sibling tools, none appear to offer the same functionality, making it distinct.
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 when property transaction history for a US address is needed, but it does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusions. The context is clear but not fully explicit.
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.
