Detect whether an email local-part is a role/group account (e.g. info, support, admin) against a bundled list. Accepts a full email address or a bare localPart. Pure; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| No | Email address (its local-part is used) | ||
| localPart | No | Local-part only (used if email is omitted) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of disclosing behavior. It proactively states 'Pure; price 0.0 (free)' and 'bundled list', revealing that the operation is side-effect-free, costs nothing, and uses a local list. This adds meaningful context beyond the schema, though it doesn't detail output format or edge cases.
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: two sentences that cover purpose, input flexibility, and key behavioral attributes (pure, free). No fluff or 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 simple detection tool with no output schema, the description provides essential information: what it detects, input options, and behavioral traits. It omits the exact output format, but given the simplicity and lack of complex schema, it remains complete enough for an agent to invoke 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?
Schema coverage is 100% with descriptions for both 'email' and 'localPart'. The description adds clarity by explaining that either a full email or bare localPart can be supplied, complementing the schema's note that localPart is used if email is omitted.
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's function: detecting whether an email local-part is a role/group account, with examples (info, support, admin). It distinguishes itself from sibling tools like email_verify_detect_disposable by explicitly focusing on role accounts.
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 indicates the tool accepts a full email or bare localPart, but does not explicitly state when to use this tool versus alternatives. It implies the use case but lacks direct comparison to sibling tools or exclusions.
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.