Validate a company-registration number against the expected format for its jurisdiction. Pure compute; returns valid flag, normalized value and reason. Price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| jurisdiction | Yes | Jurisdiction code (e.g. gb, us_de, au) | |
| companyNumber | Yes | Company number to validate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully convey behavioral traits. It states 'Pure compute' to indicate no side effects, and lists the return values (valid flag, normalized value, reason) and pricing (free). This provides sufficient transparency for a validation tool, though it could mention that it only checks format, not registry existence.
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 extremely concise: three short sentences deliver the purpose, behavioral note, return summary, and pricing. Every sentence adds value, and the most critical information (what it does) is front-loaded. 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?
Given the tool's simplicity (2 required params, no output schema, no nested objects), the description adequately covers the core task, output summary, and pricing. It does not detail potential error cases or behavior for unknown jurisdictions, but for a pure validation tool with a clear return structure, it is mostly 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%, so the schema documents both parameters with examples. The description does not add additional semantics beyond the schema; it mentions jurisdiction and company number implicitly but provides no new information about parameter format, constraints, or meaning. A score of 3 is appropriate as the description is not required to repeat schema details.
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 explicitly states the tool's purpose: validating a company-registration number against expected format for its jurisdiction. It uses specific verbs ('validate') and resources ('company-registration number'), and clearly distinguishes from sibling tools like company_registry_company_profile and company_registry_search_company, which fetch or search data rather than validate format.
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 this tool is for format validation only ('Pure compute'), but does not explicitly state when to use it versus alternatives. It does not mention that this tool does not check the existence of the company or provide additional data, which would help differentiate it from siblings. The lack of explicit 'when to use' or 'when not to use' guidance keeps this at a 3.
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 are clearly distinguished by domain prefixes (e.g., bid_watch, grant_watch) and specific action verbs. However, the high number of similarly structured watch tools could still cause confusion, though descriptions clarify exact purposes.
Every tool follows a consistent `domain_subdomain_action` pattern with underscores, e.g., `agent_audit_query`, `bid_watch_search`. Even long names like `commerce_catalog_agent_readiness_score` adhere to this structure.
With 147 tools, the server is far too broad, covering weather, carbon estimates, domain intel, and more—well beyond its stated 'Japan public-data ledgers' scope. This sheer volume overwhelms agents and dilutes focus.
The server offers many read-only tools for Japanese public data (bids, grants, licenses, etc.), but lacks create/update/delete operations for those domains. Additionally, numerous unrelated tools (e.g., carbon estimates, weather) feel tacked on, leaving gaps in core coverage.