Current weather (temperature, apparent temperature, humidity, precipitation, wind, weather code) for a lat/lon via the free Open-Meteo API. Read-only; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude (-90..90) | |
| lon | Yes | Longitude (-180..180) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool is read-only and uses the free Open-Meteo API, but does not cover rate limits, error handling, or return format. This is adequate but lacks depth.
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 a single concise sentence that front-loads the key purpose and includes all essential information: what data is returned, input format, source, and cost. 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 parameters, no output schema, no annotations), the description is fairly complete. It covers input, output variables, source, and cost. Minor gaps include error handling or return structure, but overall sufficient.
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 description coverage is 100% (both lat and lon have descriptions). The tool description does not add further meaning beyond the schema; it lists return variables but not parameter details. Baseline 3 is appropriate.
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 returns current weather data (temperature, apparent temperature, humidity, precipitation, wind, weather code) for a given lat/lon. It uses a specific verb ('current weather') and resource, and its name 'weather_risk_current_weather' distinguishes it from siblings like 'weather_risk_forecast' and 'weather_risk_air_quality'.
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 use for current weather but does not explicitly state when to use this tool versus siblings (e.g., forecast, heat index). It mentions read-only and free, but lacks explicit guidance on context or exclusions.
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.