mcp
Server Details
Email-auth diagnosis (SPF, DMARC, DKIM, MX, blacklist) with validated copy-paste fix records.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- dnsdoctor/claude-plugin
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 4.4/5 across 4 of 4 tools scored.
Tools have distinct purposes, but get_report and scan_domain could be confused: both return scan results, with scan_domain forcing a fresh scan and get_report potentially scanning if none exists. Descriptions clarify the difference, so ambiguity is low.
All tool names follow a predictable verb_noun pattern using lowercase and underscores (build_, enroll_, get_, scan_). No mixing of conventions, making the naming very consistent.
With only 4 tools, the server feels thin but not insufficient for a focused domain scanning/monitoring purpose. It could benefit from a few more tools (e.g., list, delete, update) to round out the surface.
The tool set covers core scanning and DMARC upgrade, but lacks essential operations such as listing domains, deleting records, or managing other security checks (SPF, DKIM). Significant gaps exist for a comprehensive security toolkit.
Available Tools
4 toolsbuild_dmarc_upgradeAInspect
Return a validated DMARC enforcement-upgrade record for a domain. The alignment gate (whether p=reject is safe) is derived server-side from a scan — a caller can never assert alignment. record is null when there is no honest upgrade to offer (the DMARC lookup temp-failed, or the domain already applies a policy at least as strong as this scan justifies): relay rationale and never compose a record to fill the gap. Present a returned record verbatim; a human must approve before publishing.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the burden of behavioral disclosure. It explains that the alignment gate is derived server-side and a caller cannot assert alignment, and details the null record scenarios. This provides complete transparency about tool behavior beyond what would be expected from 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 two sentences, front-loaded with the core purpose, and every sentence adds essential information. It is efficient, with no wasted words, and structurally clear.
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 single parameter, presence of an output schema (not shown but referenced), and no annotations, the description covers all necessary aspects: what the tool returns, when it returns null, and the constraint on publishing. It is complete for an AI agent to use correctly.
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 only parameter 'domain' is not explicitly described in the input schema (0% coverage), but the description's overall context makes its purpose clear. Though not explicitly calling out the parameter, the meaning is sufficiently conveyed, and the simplicity of the parameter reduces the need for extensive elaboration.
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 tool returns 'a validated DMARC enforcement-upgrade record for a domain', specifying the action, resource, and scope. It distinguishes from sibling tools like 'scan_domain' and 'enroll_monitoring_trial' which perform different tasks.
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 provides explicit guidance: when 'record' is null (due to DMARC lookup failure or existing strong policy), relay 'rationale' and never compose a record. It also instructs to present the returned record verbatim and that a human must approve before publishing, clearly defining when and how to use the tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
enroll_monitoring_trialAInspect
Ask DNS Doctor to start a monitoring trial for a human. Sends a double-opt-in email; NOTHING is created until the human clicks the link to approve. Their click creates the account and signs them in — monitoring of the domain itself starts only after they add it in the dashboard and prove ownership with a TXT record. Returns a pending-consent message; relay it to the human verbatim.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | |||
| domain | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the burden of behavioral disclosure. It reveals critical behavioral traits: nothing is created until the user clicks a link, monitoring only starts after domain ownership verification, and the tool returns a pending-consent message to relay verbatim.
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 concise (3 sentences) and front-loaded with the action. Every sentence adds value, explaining the process step by step without unnecessary detail.
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 simple parameter set (2 required) and the presence of an output schema, the description is complete. It clarifies the entire enrollment flow, return value, and agent action (relay message verbatim).
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 input schema has 0% description coverage, so the description must compensate. It mentions 'email' and 'domain' but provides no additional semantics or formatting details beyond their names, leaving ambiguity about acceptable formats or validation.
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 tool's purpose: starting a monitoring trial via DNS Doctor. It uses specific verbs ('start monitoring trial') and resources ('human', 'domain'), and distinguishes itself from sibling tools like build_dmarc_upgrade or scan_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 explains when to use the tool (to enroll a human in a monitoring trial) and the verification steps involved. However, it does not explicitly state when not to use it or provide alternative tools for comparable tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_reportAInspect
Return the persisted report for a domain, scanning once if none exists yet. Same shape as scan_domain.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses a key behavioral trait: scanning once if no report exists. However, it does not mention permissions, side effects, or rate limits.
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 extremely concise with two sentences, front-loading the key purpose and behavior, and includes a helpful reference to a sibling tool for understanding the output shape.
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 simplicity (1 parameter, output schema exists), the description covers the core behavior and output shape. However, it lacks prerequisites (e.g., domain enrollment) and error handling context.
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 input schema has 1 parameter with 0% description coverage. The description adds meaning by stating the report is 'for a domain', which clarifies the parameter's role, but does not elaborate on format or constraints.
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 'Return', the resource 'persisted report for a domain', and a unique behavior 'scanning once if none exists'. It distinguishes the tool from the sibling 'scan_domain' by referencing the output shape.
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 provides context by mentioning that the output shape is the same as 'scan_domain', but it does not explicitly state when to use this tool versus alternatives or any when-not-to use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_domainAInspect
Force a fresh scan of a domain and return its full report (deterministic verdicts + fixengine fix records; cache-first explanations). A repeat scan of the same domain within a minute reuses the stored report.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses key behaviors: forced fresh scan, caching with a 1-minute reuse window, and report content (verdicts, fix records). Could mention error handling or rate limits, but covers the main behavioral traits.
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, front-loading the core action and then detailing the result. Every word adds value, with no redundancy or fluff.
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 an output schema exists (not shown), the description need not detail return values. It explains caching and report contents. Minor gap: does not specify synchronous behavior or timeout, but overall complete for a simple scan tool.
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 single parameter 'domain' has no description in the schema (0% coverage). The description does not clarify format requirements (e.g., full URL, with or without protocol). This omission forces an agent to guess the expected format.
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 tool's purpose: force a fresh scan of a domain and return a full report. It specifies what is included (verdicts, fix records) and distinguishes from siblings like 'get_report' by emphasizing freshness and force.
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 provides clear context on when to use, highlighting fresh scans and caching behavior. However, it does not explicitly state when not to use or suggest alternatives.
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" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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