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., "@MCP URL Fetcher fetch https://news.ycombinator.com and convert it 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.
A Model Context Protocol (MCP) server that fetches content from any URL and converts it to your desired output format.
MCP URL Format Converter provides tools for retrieving content from any web URL and transforming it into various formats (HTML, JSON, Markdown, or plain text), regardless of the original content type. It's designed to work with any MCP-compatible client, including Claude for Desktop, enabling LLMs to access, transform, and analyze web content in a consistent format.
Related MCP server: MCP Web Tools Server
🔄 Format Conversion: Transform any web content to HTML, JSON, Markdown, or plain text
🌐 Universal Input Support: Handle websites, APIs, raw files, and more
🔍 Automatic Content Detection: Intelligently identifies source format
🧰 Robust Library Support: Uses industry-standard libraries:
Cheerio for HTML parsing
Marked for Markdown processing
Fast-XML-Parser for XML handling
CSVtoJSON for CSV conversion
SanitizeHTML for security
Turndown for HTML-to-Markdown conversion
🔧 Advanced Format Processing:
HTML parsing with metadata extraction
JSON pretty-printing and structure preservation
Markdown rendering with styling
CSV-to-table conversion
XML-to-JSON transformation
📜 History Tracking: Maintains logs of recently fetched URLs
🛡️ Security Focus: Content sanitization to prevent XSS attacks
Node.js 16.x or higher
npm or yarn
Clone the repository:
git clone https://github.com/yourusername/mcp-url-converter.git
cd mcp-url-converterInstall dependencies:
npm installBuild the project:
npm run buildRun the server:
npm startOpen your Claude for Desktop configuration file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Add the URL converter server to your configuration:
{
"mcpServers": {
"url-converter": {
"command": "node",
"args": ["/absolute/path/to/mcp-url-converter/build/index.js"]
}
}
}Restart Claude for Desktop
fetchFetches content from any URL and automatically detects the best output format.
Parameters:
url (string, required): The URL to fetch content from
format (string, optional): Format to convert to (auto, html, json, markdown, text). Default: auto
Example:
Can you fetch https://example.com and choose the best format to display it?fetch-jsonFetches content from any URL and converts it to JSON format.
Parameters:
url (string, required): The URL to fetch content from
prettyPrint (boolean, optional): Whether to pretty-print the JSON. Default: true
Example:
Can you fetch https://example.com and convert it to JSON format?fetch-htmlFetches content from any URL and converts it to HTML format.
Parameters:
url (string, required): The URL to fetch content from
extractText (boolean, optional): Whether to extract text content only. Default: false
Example:
Can you fetch https://api.example.com/users and convert it to HTML?fetch-markdownFetches content from any URL and converts it to Markdown format.
Parameters:
url (string, required): The URL to fetch content from
Example:
Can you fetch https://example.com and convert it to Markdown?fetch-textFetches content from any URL and converts it to plain text format.
Parameters:
url (string, required): The URL to fetch content from
Example:
Can you fetch https://example.com and convert it to plain text?web-search and deep-researchThese tools provide interfaces to Perplexity search capabilities (when supported by the MCP host).
recent-urls://listReturns a list of recently fetched URLs with timestamps and output formats.
Example:
What URLs have I fetched recently?This server implements several security measures:
HTML sanitization using sanitize-html to prevent XSS attacks
Content validation before processing
Error handling and safe defaults
Input parameter validation with Zod
Safe output encoding
You can test the server using the MCP Inspector:
npm run testConnection errors: Verify that the URL is accessible and correctly formatted
Conversion errors: Some complex content may not convert cleanly between formats
Cross-origin issues: Some websites may block requests from unknown sources
For additional debugging information, set the DEBUG environment variable:
DEBUG=mcp:* npm startThis project is licensed under the MIT License - see the LICENSE file for details.
Built with the Model Context Protocol
Uses modern, actively maintained libraries with security focus
Sanitization approach based on OWASP recommendations
Last updated: 29 March 2025
Fetch content from a URL with automatic content type detection
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Format to convert to (default: auto) | |
| url | Yes | URL to fetch content from |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions automatic content type detection, which hints at behavior, but lacks critical details like error handling, timeouts, authentication needs, rate limits, or response structure. For a tool that interacts with external URLs, this is a significant gap in behavioral disclosure.
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, efficient sentence that states the core functionality without waste. It is front-loaded and appropriately sized for the tool's purpose, making it easy to parse and understand 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 complexity of fetching from URLs, lack of annotations, and no output schema, the description is incomplete. It does not cover potential issues like network errors, content parsing, or return values, which are crucial for an agent to use the tool effectively in varied contexts.
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%, with clear descriptions for both parameters (url and format). The description adds minimal value beyond the schema, as it only implies content type detection relates to the format parameter. No additional semantics or usage examples are provided, so it meets the baseline for high 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 states the tool's purpose as fetching content from a URL with automatic content type detection, which is clear but vague about what 'content' entails. It does not distinguish from sibling tools like fetch-html, fetch-json, etc., which likely fetch specific formats, making it less specific. The verb 'fetch' is generic, and the description lacks detail on the resource or output type beyond detection.
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 no guidance on when to use this tool versus its siblings (fetch-html, fetch-json, etc.), which are explicitly named alternatives. It mentions automatic content type detection but does not specify scenarios where this is preferred over format-specific tools, leaving the agent without clear usage context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch content from any URL and convert to HTML format
| Name | Required | Description | Default |
|---|---|---|---|
| extractText | No | Whether to extract text content only (default: false) | |
| url | Yes | URL to fetch content from |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions fetching and converting to HTML, it doesn't address important behavioral aspects like error handling (e.g., what happens with invalid URLs), authentication requirements, rate limits, timeout behavior, or whether the tool performs any sanitization of the fetched HTML.
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, efficient sentence that communicates the core functionality without any wasted words. It's appropriately sized for a simple tool and front-loads the essential information.
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 relatively simple tool with good schema coverage but no annotations and no output schema, the description provides the basic purpose but lacks important contextual information. It doesn't explain what the HTML output looks like, whether it includes metadata, or how it handles different content types. The absence of output schema means the description should ideally provide some information about return values.
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, with both parameters clearly documented. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.
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 ('fetch') and resource ('content from any URL') with the specific output format ('HTML format'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like fetch-json or fetch-markdown, which presumably fetch the same content but convert to different formats.
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 no guidance on when to use this tool versus its siblings (fetch, fetch-json, fetch-markdown, fetch-text). It mentions converting to HTML format, but doesn't explain when HTML format is preferable over other formats or what distinguishes it from the generic 'fetch' tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch content from any URL and convert to JSON format
| Name | Required | Description | Default |
|---|---|---|---|
| prettyPrint | No | Whether to pretty-print the JSON (default: true) | |
| url | Yes | URL to fetch content from |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions fetching and converting to JSON but fails to describe critical behaviors such as error handling (e.g., for non-JSON content), authentication needs, rate limits, or what happens if the URL is inaccessible. This leaves significant gaps for a tool that interacts with external resources.
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, efficient sentence that front-loads the core functionality ('fetch content from any URL') and adds the key detail ('convert to JSON format') without any wasted words. It is appropriately sized for the tool's scope.
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 complexity (fetching from external URLs with potential for errors) and the absence of both annotations and an output schema, the description is insufficient. It doesn't explain return values, error cases, or behavioral nuances, making it incomplete for safe and effective use by an AI agent.
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, clearly documenting both parameters (url and prettyPrint). The description adds no additional semantic meaning beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 with a specific verb ('fetch') and resource ('content from any URL'), and specifies the output format ('convert to JSON format'). However, it doesn't explicitly differentiate from sibling tools like 'fetch-html' or 'fetch-markdown' beyond the JSON output mention, which is implied but not directly contrasted.
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 no guidance on when to use this tool versus its siblings (fetch, fetch-html, fetch-markdown, fetch-text). It lacks explicit context about alternatives, prerequisites, or exclusions, leaving the agent to infer usage based solely on the output format.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch content from any URL and convert to Markdown format
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to fetch content from |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions fetching and converting to Markdown but does not cover critical aspects such as error handling (e.g., invalid URLs, network failures), authentication needs, rate limits, or what happens if conversion fails. This leaves significant gaps in understanding the tool's 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, efficient sentence that is front-loaded with the core functionality. There is no wasted language, and it directly communicates the tool's purpose without unnecessary elaboration.
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 (fetching and converting web content) and lack of annotations and output schema, the description is incomplete. It covers the basic purpose but misses behavioral details and output expectations. However, it is adequate for a simple tool with high schema coverage, though it could benefit from more context on limitations or results.
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, with the 'url' parameter fully documented. The description adds no additional meaning beyond the schema, such as URL format constraints or examples. Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
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 specific action ('fetch content from any URL') and transformation ('convert to Markdown format'), distinguishing it from sibling tools like fetch-html, fetch-json, and fetch-text by specifying the output format. It uses precise verbs and identifies the resource (URL content).
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 when Markdown output is needed from a URL, but it does not explicitly state when to use this tool versus alternatives like fetch-html or fetch-text. There is no guidance on exclusions or prerequisites, leaving usage context to inference from the tool name and description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch content from any URL and convert to plain text format
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to fetch content from |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions fetching and converting to plain text, but lacks details on error handling, rate limits, authentication needs, or what happens with non-text content (e.g., images, binary files). For a tool with no annotations, this leaves significant gaps in understanding its behavior and constraints.
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, efficient sentence that front-loads the key information: fetching content and converting to plain text. There is no wasted verbiage, and it directly communicates the tool's core functionality without unnecessary details.
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 complexity (fetching from URLs, which can involve network issues, varied content types) and the absence of annotations and output schema, the description is incomplete. It doesn't address potential errors, output format details (e.g., how plain text is extracted), or behavioral traits like timeouts or size limits. This leaves the agent with insufficient context for reliable 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?
The input schema has 100% description coverage, with the 'url' parameter clearly documented. The description adds no additional semantic information beyond what the schema provides (e.g., it doesn't specify URL formats, protocols supported, or content types). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 content') and resource ('from any URL'), and specifies the output format ('plain text'). It distinguishes from siblings by mentioning the conversion to plain text, which suggests differentiation from fetch-html, fetch-json, and fetch-markdown. However, it doesn't explicitly name the siblings or detail how it differs from the generic 'fetch' tool, keeping it from a perfect score.
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 no guidance on when to use this tool versus its siblings (fetch, fetch-html, fetch-json, fetch-markdown). It implies usage for converting content to plain text, but doesn't specify scenarios, exclusions, or alternatives. This lack of explicit context leaves the agent to infer usage, which is insufficient for effective tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The tools have significant overlap in purpose, as all fetch content from URLs, differing only in output format. An agent could easily misselect between fetch-html, fetch-json, fetch-markdown, and fetch-text, since the descriptions don't clarify when to use one format over another. The generic 'fetch' tool with automatic detection further confuses boundaries, as it might duplicate or conflict with the format-specific tools.
Tool names follow a highly consistent verb-noun pattern throughout, with all tools using 'fetch' as the verb followed by a hyphen and format descriptor (e.g., fetch-html, fetch-json). There are no deviations in style or convention, making the naming predictable and easy to parse for an agent.
With 5 tools, the count is reasonable for a URL fetching server, but it feels borderline due to redundancy. The tools could potentially be consolidated into fewer, more flexible tools (e.g., a single fetch tool with a format parameter), making the current set feel slightly over-specified for the simple domain of fetching URLs.
For the domain of fetching URL content, the tool set is nearly complete, covering automatic detection and common output formats (HTML, JSON, Markdown, plain text). A minor gap exists in handling errors or advanced configurations (e.g., headers, timeouts), but agents can likely work around this with the provided tools for basic fetching tasks.
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