Daily weather forecast (temp max/min, precipitation probability, wind, weather code) for up to N days 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) | |
| days | No | Forecast horizon in days (bounded) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description mentions read-only and free nature, but lacks details on rate limits, pagination, or error handling. Without annotations, the description carries full burden and provides only basic behavioral context.
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 key information: what tool does, data included, 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?
Covers main points: data fields, source, read-only, free. Does not specify default for optional days parameter or error handling, but adequate for a simple forecast tool.
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 little beyond stating 'for up to N days' which reinforces the days parameter. No additional semantics for lat/lon or days beyond schema.
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?
Description clearly states the tool provides a daily weather forecast for a lat/lon, listing specific data fields (temp max/min, precipitation probability, wind, weather code). This distinguishes it from siblings like weather_risk_current_weather or weather_risk_heat_index.
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?
Description implies usage for daily forecasts via Open-Meteo API and notes it's read-only and free. However, it does not explicitly contrast with siblings or state when to use alternatives, though the context is clear enough.
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.