Fetch and parse the SPF policy from domain TXT records via DNS-over-HTTPS: presence, raw record, all-qualifier (strict/softfail/neutral/pass) and includes. Accepts a bare domain or a full email address. Read-only; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| No | Email address (its domain is used if domain is omitted) | ||
| domain | No | Domain name (bare host) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool is 'Read-only; price 0.0 (free)' and uses DNS-over-HTTPS, providing useful behavioral context. It does not detail potential failure modes or edge cases, but the safety and cost aspects are clearly stated.
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, front-loading the core function and then adding input details and cost/safety. Every word contributes meaning, with no redundancy or filler.
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?
Despite lacking an output schema, the description enumerates expected outputs (presence, raw record, all-qualifier, includes), input modes, transport mechanism, and cost/safety. For a simple read-only DNS lookup tool, this covers all essential aspects an agent needs.
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 both 'email' and 'domain' described. The description adds that it 'Accepts a bare domain or a full email address', which mirrors the schema's explanation that the email's domain is used if domain is omitted. This adds minimal value beyond the schema, warranting the baseline score.
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 'Fetch and parse the SPF policy from domain TXT records via DNS-over-HTTPS', using a specific verb and resource. It distinguishes itself from siblings like email_verify_check_mx by explicitly targeting SPF and listing specific output components (presence, raw record, all-qualifier, includes).
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 provides clear context for when to use the tool ('Fetch and parse the SPF policy') and input flexibility ('Accepts a bare domain or a full email address'). It does not explicitly name alternatives or exclusion criteria, but the context is sufficient to differentiate from related email verification tools.
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.