Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@urltomarkdown-mcp convert https://example.com to markdown"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that converts URLs and raw HTML to clean Markdown. Built on top of urltomarkdown.
Give any MCP-compatible AI assistant the ability to read web pages — just point it at a URL and get structured Markdown back, ready for summarization, analysis, or ingestion into your workflow.
convert_url_to_markdownFetches a URL and converts the web page to clean Markdown.
Parameter | Type | Required | Default | Description |
| string (URL) | yes | - | URL to fetch and convert |
| boolean | no |
| Prepend page title as H1 |
| boolean | no |
| Strip hyperlinks |
| boolean | no |
| Use Readability for cleaner output |
convert_html_to_markdownConverts raw HTML to clean Markdown (no network request needed).
Parameter | Type | Required | Default | Description |
| string | yes | - | Raw HTML to convert |
| string (URL) | no | - | Source URL for relative links and domain filters |
| boolean | no |
| Prepend page title as H1 |
| boolean | no |
| Strip hyperlinks |
| boolean | no |
| Use Readability for cleaner output |
Related MCP server: stripfeed-mcp-server
Research — Have your AI assistant read and summarize articles, documentation, or blog posts
Data extraction — Pull structured content from web pages for analysis
Documentation ingestion — Convert API docs or reference pages into Markdown for context
Content migration — Bulk convert web content to Markdown format
RAG pipelines — Feed clean web content into retrieval-augmented generation systems
{
"mcpServers": {
"urltomarkdown": {
"command": "npx",
"args": ["-y", "urltomarkdown-mcp"]
}
}
}npm install -g urltomarkdown-mcpThen add to your MCP config:
{
"mcpServers": {
"urltomarkdown": {
"command": "urltomarkdown-mcp"
}
}
}git clone https://github.com/SapienEx-AI/urltomarkdown-mcp.git
cd urltomarkdown-mcp
npm install{
"mcpServers": {
"urltomarkdown": {
"command": "node",
"args": ["/path/to/urltomarkdown-mcp/src/index.js"]
}
}
}Client | Config file |
Claude Desktop |
|
Claude Code |
|
Cursor |
|
Windsurf |
|
This server wraps the urltomarkdown library and exposes it over the Model Context Protocol via stdio transport. Under the hood it uses:
Turndown for HTML-to-Markdown conversion
Mozilla's Readability for content extraction and noise removal
JSDOM for DOM parsing
The Readability pass strips navigation, sidebars, ads, and other non-content elements before conversion, producing clean output suitable for LLM consumption.
npm testMIT
SapienEx — AI strategy and consulting
Convert raw HTML to clean Markdown
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | Raw HTML to convert | |
| url | No | Source URL for resolving relative links and domain-specific filters | |
| inline_title | No | Prepend page title as H1 heading | |
| ignore_links | No | Strip hyperlinks from output | |
| readability | No | Use Readability for cleaner output |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only says 'clean Markdown' without details on safety, idempotency, errors, or side effects. The conversion process, potential data loss, or formatting changes are not mentioned.
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 at one sentence, front-loading the purpose. Every word earns its place, though it omits beneficial context that could be added without significant bloat.
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 and no annotations, the description should explain what 'clean Markdown' entails (e.g., handling of styles, scripts, tables). It lacks details on return format, error behavior, or parameter interactions, making it incomplete for an agent to fully anticipate behavior.
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 description coverage is 100%, so the schema already documents all five parameters. The description adds no extra meaning or context beyond what the schema provides, earning the baseline score.
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 (convert) and the resource (HTML to Markdown). It distinguishes the tool from the sibling 'convert_url_to_markdown' by specifying 'raw HTML' input, implying the sibling handles URLs.
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 explicit guidance on when to use this tool versus the sibling 'convert_url_to_markdown'. The description does not mention prerequisites, limitations, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a URL and convert its web page content to clean Markdown
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to fetch and convert | |
| inline_title | No | Prepend page title as H1 heading | |
| ignore_links | No | Strip hyperlinks from output | |
| readability | No | Use Readability for cleaner output |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It mentions 'fetch' implying network access, but omits details like rate limits, timeouts, caching, or error handling. The parameter 'readability' is not explained in context, though documented in the schema. The description is minimal but not misleading.
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, well-formed sentence that communicates the core functionality without superfluous words. It is front-loaded with the action and resource, making it easy to parse quickly.
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 absence of an output schema, the description should clarify the return format. 'Clean Markdown' implies a string, but it's ambiguous. The tool is simple with 4 parameters and no nested objects, so the description covers the main action. Lacking error or edge case info prevents a perfect 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 input schema has 100% description coverage, so the schema already provides parameter meanings. The tool description adds no additional detail beyond the schema, resulting in a baseline score of 3. No extra semantics are offered.
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 ('fetch and convert') and the resource ('URL web page content') and result ('clean Markdown'). It effectively distinguishes from the sibling 'convert_html_to_markdown' by specifying URL fetching as the input method.
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 lacks explicit guidance on when to use this tool versus the sibling 'convert_html_to_markdown'. No context is provided on prerequisites, preferred scenarios, or exclusion criteria, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.1convert_html_to_markdownconvert_url_to_markdownBoth tools convert content to Markdown but accept fundamentally different inputs: raw HTML vs. a URL. Their purposes are clearly distinct, with no overlap.
Both tool names follow the consistent 'convert_<source>_to_markdown' pattern, using snake_case and clear verb-noun structure.
With only two tools, the server is minimal but well-scoped for its domain of converting to Markdown. The count matches the simple purpose.
The server covers the two primary input types: raw HTML and URL. It is lacking file-based input, but for a specialized MCP server, this coverage is reasonable.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Web scraping for AI agents. Converts URLs to clean, LLM-ready Markdown with anti-bot bypass.
Clean Markdown and AI-readability scoring for any URL. Built for AI agents.
Converts any URL to clean, LLM-ready Markdown using real Chrome browsers
Turn any URL into clean Markdown and structured data. Scrape, crawl, search and extract.
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/SapienEx-AI/urltomarkdown-mcp'
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