Detect common email typos and suggest corrections (e.g. user@gmial.com -> user@gmail.com). Covers Gmail, Yahoo, Hotmail, Outlook, iCloud, Protonmail, and more.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address to check for typos |
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 tool's role and provides an example of the input/output transformation, but it does not state the exact return format (e.g., a list of suggestions, corrected email only, or behavior when no typo is found). This is a gap for agent invocation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with an illustrative example. Every word adds value, and the structure front-loads the core purpose. No wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and no output schema, so the description should explain return values. It does not explicitly state what the tool returns or behavior for valid emails, leaving some ambiguity. The provider list and example help, but the missing output description prevents a higher score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema describes the 'email' parameter fully (100% coverage), and the description adds no extra semantic detail beyond the schema. Baseline 3 is appropriate since the schema handles parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Detect') and resource ('common email typos') with a concrete example ('user@gmial.com -> user@gmail.com'). It clearly distinguishes from sibling tools like validate_email or check_domain by focusing on suggestion, not just validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for email typo correction and lists supported providers, giving clear context. However, it does not explicitly mention when not to use it or name alternative tools, so it falls short of full guideline coverage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.