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Meerkats-Ai

NeverBounce MCP Server

by Meerkats-Ai
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

NeverBounce MCP Server

This is a Model Context Protocol (MCP) server that integrates with the NeverBounce API to provide email validation capabilities.

Features

  • Validate email addresses to check if they are valid, deliverable, and safe to send to

Related MCP server: Have I Been Pwned MCP Server

Setup

Local Setup

  1. Clone this repository

  2. Install dependencies:

    npm install

  3. Create a .env file based on .env.example and add your NeverBounce API key:

    NEVERBOUNCE_API_KEY=your_api_key_here

  4. Build the server:

    npm run build

  5. Start the server:

    npm start

Docker Setup

  1. Clone this repository

  2. Create a .env file with your NeverBounce API key

  3. Build and run using Docker Compose:

    docker-compose up -d

MCP Configuration

To use this server with an MCP client, add the following configuration to your MCP settings file:

{
  "mcpServers": {
    "neverbounce": {
      "command": "node",
      "args": ["path/to/neverbounce/dist/index.js"],
      "env": {
        "NEVERBOUNCE_API_KEY": "your_api_key_here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Available Tools

  • neverbounce_validate_email: Validate an email address to check if it's valid and deliverable

License

ISC

Available Tools

1 tool
neverbounce_validate_emailC

Validate an email address to check if it's valid and deliverable.

ParametersJSON Schema
NameRequiredDescriptionDefault
emailYesThe email address to validate
timeoutNoTimeout in milliseconds (optional, default: 30000)

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions validation but does not describe how the validation works (e.g., API call, rate limits, authentication needs, error handling, or what 'deliverable' entails). This leaves significant gaps in understanding the tool's behavior and constraints.

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

Conciseness4/5

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

The description is a single, clear sentence that efficiently states the tool's purpose without unnecessary details. It is front-loaded and wastes no words, though it could be slightly more informative without losing conciseness.

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

Completeness2/5

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

Given no annotations, no output schema, and a simple input schema, the description is incomplete. It lacks details on behavioral traits (e.g., how validation is performed, error responses), and with no output schema, it does not explain return values or result formats. This leaves the agent with insufficient context for effective use.

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%, with both parameters ('email' and 'timeout') fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or validation criteria. The baseline score of 3 reflects adequate coverage by the schema alone.

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?

The description clearly states the tool's purpose: 'Validate an email address to check if it's valid and deliverable.' It specifies the verb ('validate') and resource ('email address'), and distinguishes it from other validation types by mentioning both validity and deliverability. However, with no sibling tools provided, it cannot demonstrate explicit differentiation from alternatives.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or exclusions. It simply states what the tool does without context for application. With no sibling tools mentioned, there is no comparison to other options, leaving the agent without usage direction.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool update
    • First observedneverbounce_validate_email
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TDQS

B3.1/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

A single tool inherently follows a consistent naming pattern, as there are no other tools to compare it against. The tool name 'neverbounce_validate_email' is clear and descriptive.

Tool Count2/5

A single tool is too few for a server dedicated to email validation, as it lacks complementary operations like bulk validation, status checking, or integration with other email-related functions. This minimal scope limits functionality and may require agents to work around gaps.

Completeness2/5

The server is severely incomplete for email validation; it only offers single email validation without support for batch processing, result history, or additional verification features (e.g., disposable email detection). This will likely cause agent failures in more complex workflows.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

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