Compute the heat index (feels-like temperature) and risk category from air temperature (Celsius) and relative humidity. Pure compute; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| tempC | Yes | Air temperature in degrees Celsius | |
| humidityPct | Yes | Relative humidity percent (0-100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description says 'Pure compute' and 'free' implying no side effects, but lacks details on rate limits, authentication needs, or exact behavior (e.g., return format).
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 essential info, no filler 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?
For a simple compute tool with complete schema and no output schema, description adequately explains inputs and scope. Could mention output units or risk categories, but still clear.
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 covers both parameters with descriptions; description adds no further meaning, so baseline 3 as per guidelines.
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?
States exactly what it computes (heat index and risk category) and from which inputs (temperature, humidity). Distinguishes from sibling tools like weather_risk_air_quality or weather_risk_current_weather.
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?
Mentions it's free and pure compute but gives no guidance on when to use this vs alternatives or when not to use it.
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.