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Emailable

by io.github.pipeworx-io

Emailable Verify

emailable_verify
Read-onlyIdempotent

Verify whether an email address is deliverable (jane@stripe.com) — deliverability state, quality score, disposable/role/free flags, and typo suggestions. Uses Emailable's SMTP-level check (state is one of deliverable/undeliverable/risky/unknown; score is 0-100). A slow SMTP check can take up to 10s — that is expected. Example: emailable_verify({ email: "jane@stripe.com", apiKey: "live..." })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
smtpNoWhether to run the live SMTP deliverability check (default true). Disable to return faster, lower-confidence results.
emailYesEmail address to verify, e.g. "jane@stripe.com"
_apiKeyYesYour Emailable LIVE API key (format "live_..."). Get one at emailable.com. Use a `live_` key — `test_` keys return fake data.
timeoutNoSMTP timeout in seconds, 2-10 (default chosen by Emailable). A slow SMTP check can take up to 10s.
accept_allNoWhether to run the accept-all (catch-all) detection check.

TDQS

A4.5/5.0
Behavior5/5

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

Description adds behavioral details beyond annotations: uses SMTP-level check, state options (deliverable/undeliverable/risky/unknown), score range (0-100), and potential latency up to 10s. Annotations already indicate readOnly and idempotent, so no contradiction.

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?

Two concise sentences plus a clear example. Every sentence adds value. Information is front-loaded: purpose, outputs, source, and expected behavior.

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 5 parameters with full schema coverage and no output schema, the description adequately explains return values (state, score, flags, typos) and provides an example. No missing context for a verification tool.

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

Parameters3/5

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

Schema coverage is 100% with descriptions for all 5 parameters. The description provides an example but adds no new semantics beyond what the schema already covers, so baseline score of 3 is appropriate.

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 clearly states it verifies email deliverability, listing specific outputs (state, score, flags, typo suggestions) and provides an example invocation. This distinguishes it from sibling tools which are unrelated.

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

Usage Guidelines4/5

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

The description explicitly states the tool's purpose and mentions that a slow SMTP check can take up to 10s, which is expected. It does not explicitly exclude alternatives, but the context is clear enough for an AI agent to understand when to use this tool.

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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TDQS

A4.4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the ask_pipeworx family (standard, beta, grounded) and validate_claim vs ask_pipeworx_grounded may cause some confusion for agents despite detailed descriptions.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern, and most are verb_noun structures (e.g., ask_pipeworx, compare_entities, resolve_entity), making them predictable and easy to distinguish.

Tool Count4/5

With 32 tools, the set is larger than ideal but well-justified by the broad scope of data sources and functionalities (email verification, SEC/FDA lookups, prediction market analysis, memory, subscriptions).

Completeness5/5

The tool set covers the full pipeline from data discovery (discover_tools, suggest_questions) to retrieval, analysis, comparison, verification, and monitoring, with no obvious gaps for its intended use cases.

Resources