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, the description carries the burden. It discloses important behavioral traits: 'Format check only; no VIES call' (no external calls) and 'Pure; price 0.0 (free)' (side-effect-free and no cost). This helps the agent understand constraints, though it doesn't detail return format or error handling.
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 action, and zero waste. Every word adds meaning, from the validation scope to the free-of-charge point.
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 simple single-parameter tool with no output schema, the description covers all essential aspects: what it validates, how it operates (format only, no VIES), purity, and cost. It is fully sufficient for an agent to use correctly.
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 baseline is 3. The description adds value by explaining the expected format structure 'country prefix + national pattern', which enriches the schema's brief example. This extra semantic context justifies a 4.
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's purpose: 'Validate the structural format of an EU VAT number' with explicit scope details ('country prefix + national pattern'). It also distinguishes itself from siblings by noting 'Format check only; no VIES call', making the action and resource 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 description provides clear context for when to use this tool: for structural format validation only, not for live VIES checks. It explicitly excludes VIES calls, which implies alternatives for VIES lookups. While it doesn't name sibling tools, the exclusion is precise and actionable.
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 have clearly distinct purposes, especially the ledger-watch groups with domain prefixes. However, there are near-duplicate utilities such as content_authenticity_domain_reputation and domain_intel_reputation, and fx_tax_convert overlaps with price_oracle_fx_rate/convert. The sheer number of tools also increases the chance of selecting the wrong one, though descriptions are generally clear.
The naming is largely consistent with a <domain>_<action> or <domain>_<watch>_<action> pattern, and all names use snake_case. Minor deviations include standalone names like entity_search, kyb_report, verify_receipt, and the confusing singular/plural pair of sanction_watch_* and sanctions_screen_*. Overall, the pattern is predictable.
With 172 tools, this server is far beyond the typical well-scoped range. It bundles dozens of unrelated utility domains (weather, carbon, CVE, geo, etc.) alongside the Japan public-ledger watches, making it unwieldy for an agent to navigate. This extreme count is a major coherence problem.
For the core Japan public-ledger domain, coverage is excellent: each ledger has search, get, timeline, recent_changes, and verify_ledger, plus cross-ledger entity_search and temporal_query. The unrelated utility areas are also fairly complete for their own purposes, but the server's scope is so broad that some utilities are duplicated (e.g., multiple currency converters). Overall, no critical dead ends in the main domain.