ClearBounce Email Verification

Find Business Email Address

find_email

Read-onlyIdempotent

Find a person's business email address from their full name and company domain. Returns candidate addresses with confidence scores, based on published sources and common company address patterns. Finding is free, but candidates are pattern-based guesses until verified. After showing the candidates, tell the user that confirming deliverability costs 1 credit per candidate checked, and ask before verifying. With approval, call verify_email on candidates one at a time starting from the highest confidence, and stop at the first deliverable result.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes	Full name of the person, e.g. "Jane Porter"
`domain`	Yes	Company domain, e.g. "northwindtraders.com"

Output Schema

TableJSON Schema

Name	Required	Description
`note`	No	Guidance on verification cost and how to proceed
`status`	Yes	found_published when the address came from a published source, otherwise pattern_guesses
`best_match`	No	Highest-confidence candidate address
`candidates`	Yes	Candidate addresses, highest confidence first
`confidence`	No	Confidence of the best match, 0-100
`seen_at_company`	No	Addresses already published at this domain
`total_candidates`	Yes
`detected_company_pattern`	No	Address pattern used at this company, when known

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnly and idempotent annotations, the description discloses that candidates are unverified pattern-based guesses, that finding is free while verification costs credits, and that the tool should not verify without user approval. This is substantial behavioral context that the agent would otherwise lack.

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 front-loaded with the core purpose, then efficiently covers return behavior, cost implications, and the required verification workflow. Every sentence delivers essential operational information without padding.

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?

For a tool with two fully documented parameters and an output schema, the description covers purpose, result nature, cost, consent, and the exact follow-up action with verify_email. No critical operational detail is missing for an agent to call it correctly.

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?

The input schema already fully describes both parameters with examples, so the description's mention of full name and company domain adds limited new meaning. It reinforces the source fields but does not introduce additional parameter-level semantics beyond what the schema provides.

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 opens with a specific verb and resource: find a person's business email from full name and company domain. It clearly differentiates the tool from verify_email by describing find as generating pattern-based candidates rather than confirming deliverability.

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?

The description provides explicit workflow guidance: show candidates, inform the user about credit costs, ask before verifying, then call verify_email one at a time by confidence until a deliverable result is found. This makes the tool's role relative to verify_email and the approval process unambiguous.

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

All ClearBounce Email Verification tools Inspect

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TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a clearly distinct responsibility: real-time single verification, background bulk verification, bulk job status polling, credit balance lookup, and email discovery. The overlap between verify_email and verify_bulk is resolved by descriptions of real-time vs async batch processing.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using lowercase snake_case: verify_email, verify_bulk, check_bulk_status, check_credits, find_email. The naming convention is uniform and predictable.

Tool Count5/5

Five tools is well-scoped for an email verification service: single verification, bulk verification, status polling, credit tracking, and person email discovery. No redundant tools exist and each earns its place.

Completeness4/5

The set covers the core email verification lifecycle well, including single and bulk paths plus credit management. The only minor gap is that detailed bulk result files are accessible via the dashboard rather than the API, and there is no cancel/delete operation for bulk jobs.

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