Estimate electricity CO2e (kg) from energy use (kWh) and a regional grid-intensity factor (defaults to the IEA world average). Pure compute; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| kWh | Yes | Electricity consumed in kWh (>= 0) | |
| region | No | Grid region (default: global). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool is 'pure compute' (side-effect-free, idempotent) and 'price 0.0 (free)', which are key behavioral traits. It does not detail what happens on invalid input (e.g., negative kWh) or specify rate limits, but for a simple estimation tool, the core behavior is adequately communicated.
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?
The description is extremely concise: one sentence and a short clause totaling about 20 words. It front-loads the action and output ('Estimate electricity CO2e (kg)') with no extraneous information. Every word earns its place.
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?
Given there is no output schema, the description should fully explain the return value. It indicates the output is in 'CO2e (kg)' but does not specify the structure (e.g., a single number vs. an object), nor does it mention error handling or constraints beyond the schema. For a simple tool this may be sufficient, but more detail on the output format 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 descriptions for both parameters. The description adds meaningful context: it explains that the region parameter provides a 'regional grid-intensity factor' and clarifies that the default is the 'IEA world average'. This enhances understanding beyond the schema's enum list and default mention.
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 verb 'Estimate' and the resource 'electricity CO2e (kg)' from 'energy use (kWh) and a regional grid-intensity factor'. It differentiates from similar carbon tools (e.g., carbon_estimate_emission_factor) by specifying the computation of emissions rather than returning a factor, and it mentions the default factor source (IEA world average). The phrase 'pure compute; price 0.0 (free)' further clarifies the nature of the tool.
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 when you have kWh data and want to compute emissions for electricity, and it mentions the default regional factor. It explicitly states 'pure compute; price 0.0 (free)', which is a strong usage signal (no cost, no side effects). However, it does not explicitly state when not to use it or directly contrast it with siblings like carbon_estimate_emission_factor, which could provide clearer guidance.
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.