atdata-email-verification-mcp-server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: verify_email handles single email verification, while batch_verify_emails processes multiple emails in batch. There is no overlap or ambiguity between them, as each serves a specific use case within the email verification domain.
Naming Consistency5/5Both tools follow a consistent verb_noun naming pattern (verify_email and batch_verify_emails) that clearly indicates their function. The naming is predictable and readable, with no deviations in style or convention.
Tool Count2/5With only 2 tools, the server feels under-scoped for a comprehensive email verification service. While the tools cover basic verification, there are likely missing operations such as checking verification status, managing API keys, or handling bulk results that would enhance completeness.
Completeness2/5The toolset is severely incomplete for an email verification domain. It lacks essential operations like checking verification history, managing batch jobs, or providing domain-level insights. The current tools only offer basic verification without supporting common workflows or lifecycle management.
Average 4.3/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the batch processing approach and API key fallback behavior, but doesn't cover important aspects like rate limits, error handling, authentication requirements beyond the API key, or what happens if the API is unavailable. It provides basic operational context but lacks comprehensive behavioral details.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly structured and concise with zero wasted words. It opens with the core purpose, explains the batch nature, provides clear parameter documentation in a structured format, and describes the return value. Every sentence earns its place and information is front-loaded effectively.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with no annotations and no output schema, the description provides adequate coverage of inputs and basic operation but lacks details about the verification results format, error conditions, or API limitations. The return value description is helpful but doesn't fully compensate for the missing output schema, leaving the agent uncertain about the exact structure of verification results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by clearly explaining both parameters: 'emails' as 'List of email addresses to verify' and 'api_key' with its fallback behavior to environment variable. The description adds essential meaning beyond the bare schema, including the optional nature and default behavior of the api_key parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('verify multiple email addresses'), the resource ('using AtData's SafeToSend API'), and distinguishes from the sibling tool 'verify_email' by emphasizing batch processing. The phrase 'in batch, processing each one individually' explicitly differentiates it from single-email verification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('verify multiple email addresses in batch'), but doesn't explicitly state when not to use it or mention the sibling 'verify_email' as an alternative for single emails. The guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the core functionality and business purpose well, but lacks details about error handling, rate limits, authentication requirements beyond the API key parameter, or what happens when the API key isn't provided. The description doesn't contradict any annotations since none exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It begins with the core purpose, adds business context, then provides clear sections for Args and Returns. Every sentence earns its place, with no redundant information or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 2 parameters, no annotations, and no output schema, the description does a good job covering the essentials. It explains what the tool does, provides parameter semantics, and describes the return structure. However, it could benefit from more behavioral context about error cases or performance characteristics given the lack of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing clear semantic explanations for both parameters. It explains that 'email' is 'The email address to verify' and that 'api_key' is the 'AtData API key' with fallback behavior to environment variable. This adds significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('verify an email address'), identifies the resource ('email address'), and specifies the external service ('AtData's SafeToSend API'). It distinguishes from the sibling tool 'batch_verify_emails' by being for single email verification rather than batch processing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool ('to filter out invalid and high-risk ones, which results in higher open rates, clicks, and conversions'), but doesn't explicitly mention when NOT to use it or provide detailed alternatives. The sibling tool 'batch_verify_emails' is implied as an alternative for batch operations, but not explicitly called out.
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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