emailmd
Server Details
Render markdown into email-safe HTML, lint drafts for deliverability problems, and preview emails.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- anypost/emailmd
- GitHub Stars
- 1,286
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Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.1/5 across 3 of 3 tools scored.
Each tool has a completely distinct purpose: lint for analysis, read_docs for documentation, render for conversion. No overlap or ambiguity.
All tool names are single lowercase verbs, following a consistent and predictable pattern.
With only 3 tools, the surface is minimal but each tool serves a core function in the emailmd workflow, justifying its existence.
The tool set covers the essential operations for emailmd markdown: validation, learning, and rendering. No obvious dead ends or missing features.
Available Tools
3 toolslintLint an emailAInspect
Check emailmd markdown for deliverability, accessibility, and readability problems without rendering it: missing alt text, http:// links, Gmail's 102KB clip limit, generic link text, spam-trigger phrases, and more. Fix warnings before sending; suggestions are sometimes intentional (e.g. transactional emails without an unsubscribe link).
| Name | Required | Description | Default |
|---|---|---|---|
| markdown | Yes | The emailmd markdown document, optionally starting with YAML frontmatter. | |
| partials | No | Named markdown partials spliced in wherever the document says "::: include <name>". |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, description discloses that the tool is a read-only checker (no rendering), lists specific problem categories, and warns that some suggestions may be intentional. Adequately reveals 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?
Two sentences, efficiently front-loaded with purpose, no wasted 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?
For a linter tool with 2 params, no output schema, and no annotations, the description fully covers purpose, examples of issues, and caveats about intentional warnings. Complete enough for tool selection and use.
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 (markdown and partials). Description adds minimal extra meaning beyond schema; baseline 3 is appropriate.
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?
Clearly states the tool checks emailmd markdown for deliverability, accessibility, and readability problems without rendering. Lists specific issues (missing alt text, http:// links, Gmail clip limit, etc.) and distinguishes from sibling tools (read_docs, render).
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?
Explicitly says to fix warnings before sending and notes that suggestions are sometimes intentional (e.g., transactional emails without unsubscribe link). Provides context for when to use but does not explicitly contrast with siblings or state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_docsRead emailmd docsAInspect
Fetch emailmd documentation from emailmd.dev. Call with no arguments for the index of all pages; pass page to read one (e.g. 'buttons', 'frontmatter', 'theme', 'directives/hero'). Read the relevant page before using syntax you are not sure about.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Docs page path, e.g. 'buttons' or 'directives/hero'. Omit for the index. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description sufficiently discloses behavior (fetching remote docs, returning index or page content). No side effects or special requirements mentioned, which is acceptable for a read-only documentation tool.
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?
Two sentences, each serving a clear purpose. First sentence states action and resource, second provides usage instructions and examples. No wasted 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 the simple tool with one optional parameter and no output schema, the description is fully adequate. It explains how to get both index and individual pages.
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 has 100% description coverage for the single parameter. Description adds examples and clarifies semantics (omitting parameter returns index).
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?
Clearly states it fetches emailmd documentation from emailmd.dev. Distinguishes from sibling tools (lint, render) which have different purposes.
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?
Provides explicit guidance: call with no arguments for index, pass a page path to read a specific page. Recommends reading relevant page before using unfamiliar syntax.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
renderRender an emailAInspect
Render emailmd markdown into email-safe HTML. Returns html (the complete email document), text (the plain-text MIME part), meta (frontmatter), warnings (non-fatal repairs made while rendering; aim for none), htmlBytes, and previewUrl (a live browser preview of this exact document to share with the user).
| Name | Required | Description | Default |
|---|---|---|---|
| minify | No | Minify the HTML. Recommended for sending; helps stay under Gmail's 102KB clip limit. | |
| markdown | Yes | The emailmd markdown document, optionally starting with YAML frontmatter. | |
| partials | No | Named markdown partials spliced in wherever the document says "::: include <name>". |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It details return values (html, text, meta, warnings, htmlBytes, previewUrl) and notes that warnings indicate non-fatal repairs, providing key insights into tool behavior.
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 concise sentence but could be better structured. It efficiently lists return values without redundancy.
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 and tool purpose. It covers all key aspects, though it could mention any prerequisites or constraints.
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%, so baseline is 3. The description does not add significant meaning beyond schema for parameters, though it provides useful return value context.
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 action (render) and resource (emailmd markdown into email-safe HTML), and distinguishes itself from sibling tools (lint and read_docs) which serve different purposes.
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?
No guidance on when to use this tool versus alternatives, or prerequisites. It only describes what it does, missing explicit context for selection.
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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