Compare two organization/company names and receive a match score from 0-100 indicating similarity. Useful for determining if two company names refer to the same entity. Cost: $0.01 USDC via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| org1 | Yes | First organization name | |
| org2 | Yes | Second organization name to compare |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the cost ($0.01 USDC), which is a behavioral trait beyond annotations. However, it does not explain the destructiveHint true annotation or idempotentHint false. Annotations already cover read-only and destructive hints, so the description adds limited context.
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 with two sentences. The first sentence front-loads the primary action, and every sentence adds value. No wasted words.
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 implies the return is a match score but does not specify the exact format (e.g., JSON). It lacks details on error handling or edge cases. For a simple tool, it is adequate but not fully 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 coverage is 100% and both parameters are clearly described in the schema. The description adds no extra meaning beyond stating they are organization names, so baseline 3 applies.
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 compares two organization names and returns a match score from 0-100. It uses a specific verb, resource, and outcome, and distinguishes from siblings like address or fullname matching tools.
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 a use case ('determining if two company names refer to the same entity') but does not mention when not to use it or compare to alternative siblings like interzoid_company_match_advanced. Guidance is implied rather than 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.
