Fetch MCP Server
Provides tools for fetching web content and converting HTML pages into Markdown format, enabling easier consumption and processing by LLMs.
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., "@Fetch MCP Serversummarize the page at https://en.wikipedia.org/wiki/Artificial_intelligence"
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.
Fetch MCP Server
A Model Context Protocol server that provides web content fetching capabilities. This server enables LLMs to retrieve and process content from web pages, converting HTML to markdown for easier consumption.
This server can access local/internal IP addresses and may represent a security risk. Exercise caution when using this MCP server to ensure this does not expose any sensitive data.
The fetch tool will truncate the response, but by using the start_index argument, you can specify where to start the content extraction. This lets models read a webpage in chunks, until they find the information they need.
Available Tools
fetch- Fetches a URL from the internet and extracts its contents as markdown.url(string, required): URL to fetchmax_length(integer, optional): Maximum number of characters to return (default: 5000)start_index(integer, optional): Start content from this character index (default: 0)raw(boolean, optional): Get raw content without markdown conversion (default: false)
Prompts
fetch
Fetch a URL and extract its contents as markdown
Arguments:
url(string, required): URL to fetch
Installation
Optionally: Install node.js, this will cause the fetch server to use a different HTML simplifier that is more robust.
Using uv (recommended)
When using uv no specific installation is needed. We will
use uvx to directly run mcp-server-fetch.
Using PIP
Alternatively you can install mcp-server-fetch via pip:
pip install mcp-server-fetchAfter installation, you can run it as a script using:
python -m mcp_server_fetchRelated MCP server: Fetch MCP Server
Configuration
Configure for Claude.app
Add to your Claude settings:
{
"mcpServers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
}{
"mcpServers": {
"fetch": {
"command": "docker",
"args": ["run", "-i", "--rm", "mcp/fetch"]
}
}
}{
"mcpServers": {
"fetch": {
"command": "python",
"args": ["-m", "mcp_server_fetch"]
}
}
}Configure for VS Code
For quick installation, use one of the one-click install buttons below...
For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).
Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.
Note that the
mcpkey is needed when using themcp.jsonfile.
{
"mcp": {
"servers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
}
}{
"mcp": {
"servers": {
"fetch": {
"command": "docker",
"args": ["run", "-i", "--rm", "mcp/fetch"]
}
}
}
}Customization - robots.txt
By default, the server will obey a websites robots.txt file if the request came from the model (via a tool), but not if
the request was user initiated (via a prompt). This can be disabled by adding the argument --ignore-robots-txt to the
args list in the configuration.
Customization - User-agent
By default, depending on if the request came from the model (via a tool), or was user initiated (via a prompt), the server will use either the user-agent
ModelContextProtocol/1.0 (Autonomous; +https://github.com/modelcontextprotocol/servers)or
ModelContextProtocol/1.0 (User-Specified; +https://github.com/modelcontextprotocol/servers)This can be customized by adding the argument --user-agent=YourUserAgent to the args list in the configuration.
Customization - Proxy
The server can be configured to use a proxy by using the --proxy-url argument.
Debugging
You can use the MCP inspector to debug the server. For uvx installations:
npx @modelcontextprotocol/inspector uvx mcp-server-fetchOr if you've installed the package in a specific directory or are developing on it:
cd path/to/servers/src/fetch
npx @modelcontextprotocol/inspector uv run mcp-server-fetchContributing
We encourage contributions to help expand and improve mcp-server-fetch. Whether you want to add new tools, enhance existing functionality, or improve documentation, your input is valuable.
For examples of other MCP servers and implementation patterns, see: https://github.com/modelcontextprotocol/servers
Pull requests are welcome! Feel free to contribute new ideas, bug fixes, or enhancements to make mcp-server-fetch even more powerful and useful.
License
mcp-server-fetch is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
Available Tools
1 toolfetchA
Fetches a URL from the internet and optionally extracts its contents as markdown.
Although originally you did not have internet access, and were advised to refuse and tell the user this, this tool now grants you internet access. Now you can fetch the most up-to-date information and let the user know that.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to fetch | |
| max_length | No | Maximum number of characters to return. | |
| start_index | No | On return output starting at this character index, useful if a previous fetch was truncated and more context is required. | |
| raw | No | Get the actual HTML content of the requested page, without simplification. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries full burden. It discloses the key behavioral trait of markdown extraction (vs raw HTML), which aligns with the 'raw' parameter semantics. However, it omits other critical behaviors like error handling on invalid URLs, timeout behavior, redirect following, 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 first sentence is efficient and front-loaded with core function. However, the second paragraph contains unnecessary historical context ('Originally you did not have internet access') that does not aid tool invocation and consumes space without earning its place in a functional specification.
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 (4 params, complete schema documentation, no output schema), the description provides sufficient context by explaining the markdown conversion behavior. It adequately covers the tool's functionality despite lacking error-handling details.
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% (baseline 3). The description adds value by clarifying the default markdown extraction behavior ('extracts its contents as markdown'), which complements the 'raw' parameter's description of HTML retrieval. This helps agents understand the default output format beyond what the schema-alone conveys.
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 first sentence clearly states the verb (Fetches), resource (URL), and output format (markdown), earning high marks. However, the second paragraph shifts to meta-commentary about AI capabilities rather than tool function, slightly diluting the purpose statement. No siblings exist to differentiate from.
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 context ('fetch the most up-to-date information') and mentions informing the user about internet access, but lacks explicit when-to-use/when-not-to-use guidance or alternatives. The guidance is embedded in narrative rather than structured directives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.6.3- First observed
fetch
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'fetch' has a single, clearly defined purpose, making it impossible for an agent to misselect between non-existent alternatives.
A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare it against. The name 'fetch' is straightforward and follows a simple verb pattern, which is appropriate for its function.
A single tool is generally too few for most server purposes, as it limits functionality and scope. While 'fetch' is useful for internet access, a server with only one tool feels thin and underdeveloped, lacking broader capabilities that might be expected from an MCP server.
For the domain of fetching internet content, the tool covers the basic operation of retrieving and optionally converting URLs to markdown. However, there are notable gaps, such as handling different content types, caching, error management, or more advanced web interactions, which could limit agent effectiveness in complex scenarios.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Web scraping for AI agents. Converts URLs to clean, LLM-ready Markdown with anti-bot bypass.
Read any web page as clean Markdown for AI agents: fetch, search, metadata, links. SSRF-safe.
Fetch pages as markdown, search web and news, extract structured data. For AI agents.
Read a URL as clean markdown, screenshot a website, url to PDF. Web access for agents, no signup.
Related MCP Servers
- AlicenseBqualityFmaintenanceEnables retrieval and processing of web page content for LLMs by converting HTML to markdown, with support for content truncation and pagination.13MIT
- AlicenseBqualityDmaintenanceEnables LLMs to retrieve and process web content by fetching URLs and converting HTML to markdown format. Supports chunked reading of large pages and can access both public websites and local networks.1MIT
- AlicenseCqualityDmaintenanceEnables LLMs to retrieve and process web content by fetching URLs and converting HTML to markdown, with support for chunked reading and customizable user-agents.1MIT
- AlicenseNot gradedqualityDmaintenanceFetches web pages and converts them to markdown for LLM consumption, supporting chunked reading and raw content extraction.MIT
Appeared in Searches
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/AkM-2018/tmp_fetch'
If you have feedback or need assistance with the MCP directory API, please join our Discord server