Estimate passenger-travel CO2e (kg) from distance and travel mode using embedded per-passenger-km factors. Pure compute; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| mode | Yes | Passenger travel mode. | |
| distanceKm | Yes | Travel distance in kilometres (>= 0) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description says 'pure compute' and 'free', but doesn't disclose limits (e.g., distance bounds, mode assumptions) or whether results are cached. Adequate but not detailed.
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, direct and efficient. No fluff.
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?
Lacks output description (likely a number), and doesn't specify if distance is one-way or round trip, or handling of mixed modes. Adequate for simple tool but not complete.
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 description adds minimal value beyond naming inputs. Mentions 'embedded per-passenger-km factors' but doesn't elaborate on factor sources.
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 it estimates CO2e (kg) for passenger travel from distance and mode, using embedded factors. It distinguishes from siblings like carbon_estimate_shipping_emissions.
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 pure compute and free, implying it's a lightweight calculation. No explicit when-to-use vs alternatives, but sibling names provide 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.