Estimate voluntary-market offset cost (USD) and tree- / forest-year equivalents for a given kg CO2e, using published constants. Pure compute; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| kgCO2e | Yes | Emissions to offset, in kg CO2e (>= 0) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description states 'Pure compute; price 0.0 (free)', indicating no side effects and statelessness. This adds transparency about the tool's behavior, though it could mention that no external calls are made.
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 that front-load the main purpose and key behavioral trait (free pure compute). Every word adds value, no redundancy.
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 single-parameter, no output schema tool, the description provides sufficient context: input is kg CO2e, output is USD cost and tree/forest-year equivalents, and the computation is based on published constants. Nothing is missing for an agent to use it correctly.
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?
The schema already documents the single parameter kgCO2e with description. The description adds context about output (USD, tree-year equivalents) but does not add meaning to the parameter beyond the schema. Baseline 3 applies due to 100% 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 estimates voluntary-market offset cost (USD) and tree/forest-year equivalents for a given kg CO2e, using published constants. It differentiates from sibling carbon_estimate tools (e.g., compute_emissions) by focusing on offset estimation.
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?
The description implies usage for offset cost estimation but does not explicitly state when to use or when not to use, nor does it provide alternatives. The pure compute nature is noted, but guidance on when to prefer this over other carbon tools is lacking.
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.