List the public holidays for a country in a given year from the built-in dataset. Pure/offline; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| year | Yes | Four-digit year | |
| country | No | ISO 3166 country code (default: US) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the built-in dataset origin, offline execution, and free cost, which are non-obvious behavioral traits. It does not mention error handling or output format, but for a simple listing operation this is acceptable disclosure.
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 concise sentences with the primary function front-loaded. The second sentence provides useful qualifiers (built-in dataset, offline, free) without any redundant phrasing.
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 low complexity with a fully documented schema. The description gives enough context for an agent to understand what it does and when to call it. A minor gap is not explicitly referencing sibling holiday tools, but the purpose is self-explanatory.
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% since both parameters have clear descriptions (year format, country default). The description adds little beyond restating 'in a given year' and 'country', so it does not elevate semantic clarity beyond the schema baseline.
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 states the specific verb 'List' and resource 'public holidays' with clear scope (country, year). It inherently distinguishes this from sibling tools like holiday_calendar_is_holiday or holiday_calendar_business_days_between, which handle single-date checks or date ranges.
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 clarifies the tool produces a year-level list from a built-in dataset, with the additional 'Pure/offline; price 0.0 (free)' qualifying its suitability. It does not explicitly name sibling alternatives, but the purpose is clear enough to guide selection.
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.