Validate and normalize an email address (syntax/structure only): returns valid, normalized address, local-part and domain, plus a reason on failure. Pure; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address to validate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states 'Pure' (no side effects) and 'price 0.0 (free)', and describes the return behavior including a reason on failure. This goes beyond the schema and gives the agent confidence about side effects and cost, though it could elaborate on what 'normalize' entails.
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 two sentences and front-loaded with the core purpose ('Validate and normalize an email address'). Every clause adds value: scope restriction, return values, purity, and pricing. There is no empty filler 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 one-parameter tool with no output schema, the description is reasonably complete. It covers the operation, the scope, the purity, and the return values (including failure handling). It could be more explicit about the exact return structure, but the absence of an output schema makes the provided detail sufficient for an agent to invoke 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 input schema has 100% coverage for the single 'email' parameter with the description 'Email address to validate'. The tool description adds no additional parameter-specific details (e.g., format, normalization behavior), so the baseline of 3 is appropriate because the schema already fully documents the parameter.
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 uses the verb 'Validate and normalize' and specifies 'syntax/structure only', which distinguishes this tool from sibling email verification tools like check_mx, check_spf, and detect_disposable. It also lists concrete outputs (normalized address, local-part, domain, reason on failure), leaving no ambiguity about its purpose.
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 phrase 'syntax/structure only' provides clear context that this tool is for syntax validation, not for MX, SPF, or disposable checks. While it does not explicitly name alternative tools, the sibling names are self-explanatory and the scope is unambiguous. No exclusions are stated, but the intent is conveyed.
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.