validate_batch
Batch validate up to 100 email addresses at once. Returns individual results plus summary stats.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| emails | Yes | Array of email addresses (max 100) |
email-validator
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.8/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the batch behavior, the maximum count, and the high-level return format. However, it does not mention error handling, side effects, or any constraints beyond the count limit, leaving room for more transparency.
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 crisp sentences, front-loaded with the action and resource. Every word adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema or annotations, the description covers the core behavior, constraints, and return summary. It lacks details on error representation or edge cases, but is reasonably complete given the tool's simplicity.
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 coverage is 100% and the description adds no new insight beyond what the schema already provides for the 'emails' array. The description reinforces the max 100 but does not explain parameter meaning in more depth.
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?
States the specific verb 'validate' and resource 'email addresses', and adds 'Batch' to distinguish from sibling tools like validate_email. Mentions the limit of 100 and the output of individual results plus summary stats, making the purpose unambiguous.
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?
Implies usage for validating multiple email addresses at once, but does not explicitly compare with sibling tools or state when not to use it. The phrase 'at once' gives some context, but no clear alternative/exclusion guidance is provided.
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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TDQS
A3.7/5.0
Disambiguation2/5
validate_email includes comprehensive checks (syntax, MX, SPF/DKIM, typo suggestions, risk scoring) that subsume both check_domain and suggest_fix, making the targeted tools redundant for many use cases. validate_batch is clearly separate, but the overlap between the individual validation tools and the single comprehensive validator creates ambiguity.
Naming Consistency4/5
All tool names follow a consistent verb_noun snake_case pattern (check_domain, suggest_fix, validate_batch, validate_email). The verbs differ appropriately for their actions, though validate_batch and validate_email share the same verb while the others use distinct verbs, which is a minor deviation but still predictable.
Tool Count5/5
With 4 tools, the server is well-scoped for an email validation service. Each tool has a clear purpose (single validation, batch validation, domain reputation, typo suggestions), and the count is neither sparse nor bloated.
Completeness4/5
The core email validation workflow is well covered: single check, batch processing, domain reputation, and typo correction. A minor gap is the lack of a dedicated syntax-only or MX-only validator, but validate_email already provides comprehensive checks, so most users will not encounter dead ends.