Great-circle (haversine) distance between two lat/lon coordinates, in kilometres and miles. Pure math; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| lat1 | Yes | First point latitude | |
| lat2 | Yes | Second point latitude | |
| lon1 | Yes | First point longitude | |
| lon2 | Yes | Second point longitude |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses it is 'pure math' and 'free', signaling no side effects or cost. This is useful beyond the input schema, though it does not mention error handling or coordinate limits.
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 covering the algorithm, inputs, outputs, and pricing. No wasted words; front-loaded with core functionality.
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 description implies output in both km and miles but does not specify the return format (single value vs. object). Without an output schema, more explicit structure would improve completeness.
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% with each parameter described (e.g., 'First point latitude'). The description adds no additional meaning, sticking to 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 clearly states the tool computes great-circle distance between two coordinates and outputs both kilometres and miles. It distinguishes itself from sibling geo_intel tools which are for geocoding, timezone, etc., though no explicit comparison is made.
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 guidance on when to use this tool versus alternatives like geo_intel_geocode. It only describes what it does, leaving the agent to infer usage context.
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.