Fairquery Agent Trust & Data Safety

Check email syntax, typos, disposable & role (free)

email_check_syntax

Read-onlyIdempotent

WHAT: Offline email checks: RFC-style syntax + normalization, common domain typo "did you mean" (gmial.com -> gmail.com), disposable/temporary domain (bundled CC0 list, ~thousands of domains), role account (info@, sales@), free webmail provider. WHEN: Fast pre-filtering of signups or lead lists, before paying for email_validate_deliverability. WHEN NOT: Does NOT check DNS/MX, so it cannot tell whether the domain receives mail; use email_validate_deliverability (paid) for MX/SPF/DMARC. COST: Free; no token. RETURNS: Paged "email_check" rows; verdict is invalid | risky | unknown, never valid (dns_checked is always false). BEHAVIOR: Runs on this Fairquery-hosted Worker (Cloudflare), not on the caller's machine; inputs leave the client. This Worker's code is designed not to store inputs, results, or an Apify token. No further network call (no DNS/SMTP); addresses are not forwarded. Synchronous and CPU-bound, no rate limits: 10,000 addresses (the per-call maximum) return in well under a second. PARAMETERS: emails: each element is parsed on its own (one address per element; a comma-separated list in one element is INVALID_SYNTAX, not split). Before checking, "Name " is reduced to addr, a leading "mailto:" is dropped, the domain is lowercased (local part keeps its case), a trailing dot is removed and IDN domains are converted to punycode; the normalized value is returned as email, the original as input. Duplicates are not merged: every element yields one row, in input order. Role detection ignores +tags (sales+eu@ counts as sales@) and disposable matching also covers subdomains of listed domains. limit/offset page those rows; meta.summary always counts verdicts over ALL rows, not just the current page. EXAMPLE: {"emails":["someone@gmial.com"]} -> verdict "risky", reasons ["POSSIBLE_TYPO","DNS_NOT_CHECKED"], did_you_mean "someone@gmail.com".

Input Schema

Name	Required	Description
`limit`	No	Optional page size: items to return, integer 1-1000 (default 100). Example: 50
`emails`	Yes	Required. 1-10000 strings, 1-320 chars each, one address per element; bare "jane@acme.com", "Jane <jane@acme.com>" and "mailto:jane@acme.com" forms accepted. Example: ["jane@acme.com","bob@gmial.com"]
`offset`	No	Optional zero-based number of items to skip, integer 0-1000000 (default 0); pass nextOffset from the previous response, e.g. 100

Output Schema

Name	Required	Description
`meta`	No	Tool-specific context (summary counts, cost notes, ...)
`count`	Yes	Number of items in this page
`items`	Yes	Result rows for this page
`total`	Yes	Total items before paging
`offset`	Yes	Offset of the first item in this page
`hasMore`	Yes	True if more items exist after this page
`nextOffset`	Yes	Offset to pass for the next page, or null

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 27, 2026

TDQS

A5/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds substantial behavioral context beyond annotations: it runs on a hosted Worker, inputs leave the client, it is designed not to store inputs/results/tokens, makes no DNS/SMTP network calls, is synchronous and CPU-bound, and has no rate limits with 10,000 addresses returning quickly. It also discloses that verdicts never include valid because DNS is not checked. These details are consistent with readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but front-loaded and organized under clear labels (WHAT, WHEN, WHEN NOT, COST, RETURNS, BEHAVIOR, PARAMETERS, EXAMPLE). Each section carries operational value for a tool with several edge cases, so the length is justified rather than redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, the schema, annotations, and output schema, the description is complete enough for an agent to call it correctly. It covers purpose, exclusions, normalization edge cases, paging behavior, cost, execution environment, and expected return semantics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is already 100%, the description adds important semantics: each array element is parsed independently, comma-separated lists in one element are invalid, normalization rules are specified, duplicates are not merged, rows preserve input order, and limit/offset page rows while meta.summary always counts all rows. This meaningfully extends the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states specific offline email checks: syntax/normalization, typo suggestions, disposable domains, role accounts, and free webmail. It explicitly distinguishes this pre-filter from the paid delivery-validation sibling, so an agent can route correctly without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit WHEN and WHEN NOT guidance, naming email_validate_deliverability as the alternative for DNS/MX/SPF/DMARC checks. The use case is framed clearly as fast pre-filtering of signups or lead lists before paying for delivery validation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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