Validate the structural format of an EU VAT number (country prefix + national pattern). Format check only; no VIES call. Pure; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| vatNumber | Yes | VAT number to validate (e.g. DE123456789) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It adds important behavioral traits: 'Pure' (no side effects), 'price 0.0 (free)' (no cost), and 'no VIES call' (no network call). This goes beyond the schema and gives the agent critical confidence. Still, it omits return value structure, which is a minor gap.
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?
Two sentences, front-loaded with the core purpose, followed by essential constraints. No filler or redundant information.
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?
The tool is simple with one parameter and no output schema or annotations. The description covers purpose, scope ('format check only'), absence of VIES, and purity/cost. However, it does not describe the return value or how results are reported, which is important for an agent to interpret the validation result. This omission prevents a higher score.
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 schema covers the single vatNumber parameter with a clear description and example, achieving 100% coverage. The description adds light context about the 'country prefix + national pattern' but does not significantly enhance the schema. Baseline of 3 is appropriate since schema already does the heavy lifting.
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 validates the structural format of an EU VAT number, specifying the country prefix and national pattern. It distinguishes itself from potential sibling tools by explicitly noting 'no VIES call', making the purpose unambiguous.
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 phrase 'Format check only; no VIES call' gives clear context that this tool is for structural validation and not live VIES validation, implying when it should be used. However, it does not name an alternative tool or explicitly state when not to use it, so it misses the full 'when/when-not' guidance.
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.