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notprathap

NoBounce Email Validation MCP Server

by notprathap

validate_emails_bulk

Bulk validate up to 1,000 email addresses per call by checking syntax, MX records, disposable domains, role accounts, free providers, and typos, with automatic partial-quota handling.

Instructions

Validate up to 1,000 email addresses in a single batch call. Checks syntax, MX records, disposable domains, role accounts, free providers, and typo suggestions with automatic partial-quota handling.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailsYesList of email addresses to validate (up to 1,000 per call)
api_keyNoOptional override for NOBOUNCE_API_KEY environment variable
smtp_checkNoOptional deep SMTP handshake check for each email (default: false)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It usefully discloses the actual check suite (syntax, MX, disposable, role, free, typo) and mentions 'automatic partial-quota handling,' which is real behavioral context. It still omits cost/rate-limit behavior, partial-failure semantics, and the fact that an api_key override exists, so it is only partially covers the full behavioral profile.

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 sentences, zero filler, with the batch scope and the check inventory front-loaded. Every clause carries information.

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

Completeness3/5

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

There is no output schema and no annotations, so the description would ideally say what a validated result looks like and how quota/partial results surface. It fully covers what gets checked but leaves the return shape and failure semantics unspecified for a 3-parameter 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 description coverage is 100%, so the schema already documents emails, api_key, and smtp_check — including the SMTP deep-check flag and its default. The description adds no parameter-level detail beyond what the schema provides, so the baseline 3 applies.

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

Purpose4/5

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

States a specific verb (validate) and resource (email addresses), plus the batch scale ('up to 1,000 in a single batch call'), which implicitly separates it from the single-address sibling. It stops short of naming validate_email outright, so sibling differentiation is inferential rather than explicit.

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

Usage Guidelines3/5

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

'In a single batch call' and the 1,000 cap imply the tool is for bulk work while validate_email handles singles, but no explicit when-to-use/when-not statement or named alternative is given. Usage is left to inference.

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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