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Glama
Baneado98

email-verify

by Baneado98
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are clearly distinct: one verifies a single email address, the other verifies a batch. There is no ambiguity or overlap.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: 'verify_email' and 'verify_many'. The naming is predictable and clear.

    Tool Count4/5

    Two tools is appropriate for the focused domain of email verification. While more tools could be added (e.g., domain check), the current count is reasonable for the core functionality.

    Completeness5/5

    The server covers both single and batch verification, which are the primary use cases for email verification. No obvious gaps in the tool surface for its stated purpose.

  • Average 4.4/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 7 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
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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?

    No annotations are provided, so the description must carry the burden. It discloses that the tool returns verdicts per address and mentions flag types. However, it lacks details on error handling, rate limits, or the effect of the 'deep' parameter beyond schema description.

    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, no wasted words. The first sentence states purpose and output; the second provides usage context and examples of flags.

    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?

    The description is incomplete regarding the output structure (what does a verdict look like?) and lacks details on error states, rate limits, or prerequisites. For a batch tool, these are important for correct agent invocation.

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

    Parameters4/5

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

    Schema coverage is 100%, but the description adds value by explaining the batch context, the purpose of verification (flagging risky addresses), and elaborating on the 'deep' parameter as a live SMTP probe, which is not in the schema description.

    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 the tool verifies a batch of email addresses (e.g., mailing list) and returns one verdict per address. It explicitly distinguishes from the single-verify sibling tool verify_email by focusing on 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 Guidelines4/5

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

    The description provides a clear use case: 'clean a list before a campaign' and describes what flags are returned (invalid, disposable, role-based, risky). It implies when not to use (single addresses) but does not explicitly exclude or mention alternatives beyond the sibling name.

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

  • Behavior5/5

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

    With no annotations provided, the description fully carries the burden of transparency. It details every check (syntax, MX, disposable, role, catch-all, deep SMTP probe) and explains behaviors like 'live SMTP RCPT-TO probe' that does not send email, and graceful degradation if port 25 is blocked. No contradictions.

    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 main purpose in the first sentence, uses a well-organized list for the checks, and has no extraneous words. Every sentence adds value, making it highly efficient.

    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?

    Despite lacking an output schema, the description fully defines the return values (VERDICT, score, reasons). All parameters are covered, and the tool's complexity is addressed comprehensively. No gaps remain.

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

    Parameters4/5

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

    Schema coverage is 100%, so baseline is 3. The description adds significant value beyond the schema: it explains the 'deep' parameter in detail (live probe, slowness, graceful degradation) and provides an example email format. This elevates it above baseline.

    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 starts with a clear verb+resource: 'Verify whether an email address is real and deliverable, live.' It enumerates specific checks and the verdict types, fully distinguishing it from the sibling tool 'verify_many' by focusing on 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 Guidelines4/5

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

    The description explicitly states when to use this tool: 'before adding an email to a list, accepting a signup, or sending mail you don't want to bounce.' It implies the sibling tool is for batch verification, but does not explicitly provide when-not-to-use or alternative scenarios, though the context is clear enough.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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