Get an email trust score (0-99) and AI-generated risk analysis. Validates deliverability, identifies disposable addresses, and assesses legitimacy. Premium API. Cost: $0.25 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| lookup | Yes | Email address to score and validate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include destructiveHint=true and readOnlyHint=false, yet the description uses 'Get' which implies a read-only, non-destructive operation. This contradicts the annotations, making it misleading. The description does not disclose the behavioral trait of being potentially destructive or non-idempotent.
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?
Three sentences covering purpose, capabilities, and cost. No fluff, front-loaded with the main action. Every sentence adds value.
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?
For a single-param tool with no output schema, the description gives a solid overview of functionality and cost. It lacks details on the output format, but the high schema coverage compensates. The contradiction with annotations is the main gap.
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% with a clear parameter description. The description adds no additional meaning beyond the schema, so baseline 3 is appropriate.
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 it gets an email trust score (0-99) and AI-generated risk analysis, with specific capabilities like validating deliverability and identifying disposable addresses. It distinguishes itself from siblings which cover different data lookups.
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 provides context about the tool being a premium API with a cost, implying it's for important or high-stakes email validation. It does not explicitly note when not to use it or suggest alternatives, but siblings are clearly different types of data.
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.
