mailverdict
Server Details
Keyless email checks: disposable, role, and free-provider detection, MX, and typo suggestions.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- mailverdict/mailverdict
- GitHub Stars
- 0
- Server Listing
- mailverdict
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Tool Definition Quality
Average 3.7/5 across 2 of 2 tools scored.
The two tools are clearly distinguished by input type: check_domain accepts a domain, check_email accepts an email address. There is some overlap in domain-level checks, but the different inputs and return structures prevent confusion.
Both tools follow the consistent verb_noun pattern: check_domain and check_email, making them predictable and easy to understand.
With only two tools, the server feels slightly under-scoped for a full email verification service. However, each tool packs significant functionality, so the count is borderline acceptable.
The tools cover domain and email reputation checks including disposable detection, MX records, and syntax validation. Missing a common SMTP verification step, which is a notable gap for email verification servers.
Available Tools
2 toolscheck_domainAInspect
Check a domain: disposable/burner list membership, free-provider status, typo suggestion, MX records.
| Name | Required | Description | Default |
|---|---|---|---|
| mx | No | Look up MX records (default true). | |
| domain | Yes | The domain to check. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It states the checks performed but lacks details on behavior such as return format, error handling, or whether it is read-only. This is a gap for a tool with no annotations.
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 a single sentence that front-loads the core purpose and lists specific features. Every word earns its place with no verbosity.
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?
Given the tool's moderate complexity and lack of output schema, the description enumerates the key checks. However, it does not specify the return structure or the format of typo suggestions, leaving some ambiguity.
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 provides descriptions for both parameters, so the description does not need to add much. The description's mention of MX records maps to the 'mx' parameter, adding minimal value beyond the schema. Baseline 3 applies due to 100% schema coverage.
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 clearly states the verb 'check' and the resource 'domain', and enumerates specific checks (disposable/burner, free-provider, typo suggestion, MX records). This distinguishes it from the sibling tool 'check_email', which presumably checks email addresses.
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 lists what the tool checks but does not explicitly state when to use it versus the sibling tool 'check_email'. There are no when-not or alternative directives, relying on implied differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_emailAInspect
Check an email address: syntax, disposable/burner domain, role account, free provider, typo suggestion, MX records. Returns result (deliverable|undeliverable|risky|unknown), reason, score 0-100, and per-signal booleans.
| Name | Required | Description | Default |
|---|---|---|---|
| mx | No | Look up MX records (default true). | |
| Yes | The email address to check. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It lists the checks performed and return fields (result, reason, score, booleans), but does not disclose side effects, auth needs, or whether it is read-only.
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: the first lists capabilities, the second explains the return payload. It is front-loaded, efficient, and contains no unnecessary words.
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?
Given no output schema, the description adequately explains return values (result, reason, score, per-signal booleans). However, it lacks details on error handling or edge cases like invalid email formats.
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?
Schema coverage is 100% with descriptions for both parameters. The description adds context by grouping checks but does not provide additional semantics beyond what the schema already states.
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 clearly states it checks an email address for syntax, disposable domains, role accounts, free providers, typo suggestions, and MX records, which is specific and distinguishes it from the sibling tool 'check_domain'.
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 use for email validation but does not explicitly state when to use this versus 'check_domain' or provide exclusion criteria. The intended context is clear but not formally outlined.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
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