Air-quality snapshot (PM2.5, PM10, US / European AQI and a coarse category) for a lat/lon via the free Open-Meteo air-quality 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?
Discloses read-only and free nature, but with no annotations, it omits details like API key requirements, error handling, output format, or data recency.
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 efficient sentences front-loading purpose and key traits (read-only, free), 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 core data fields and source, but lacks details on response structure, error behavior, or rate limits; acceptable for a simple read tool without output schema.
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?
Input schema covers both lat/lon with descriptions; the description adds 'for a lat/lon' but no extra meaning beyond existing schema coverage.
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 provides an 'Air-quality snapshot' with specific metrics (PM2.5, PM10, US/European AQI, coarse category) for a lat/lon, identifying it as a read-only data retrieval tool distinct from sibling weather tools like forecast or 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?
No explicit guidance on when to use this over alternatives like weather_risk_current_weather; it implies air quality monitoring but lacks when-not-to-use or alternative comparisons.
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.