A
licenseB
qualityD
maintenanceEnables 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.
1
MIT
Fetch MCP Server
by ExactDoug
The Fetch MCP Server allows you to fetch and process web content for LLMs, converting HTML to markdown for easier consumption.
Fetch Content: Retrieve content from any given URL on the internet.
Markdown Conversion: Automatically convert fetched HTML content to markdown for easier readability.
Raw HTML Option: Optionally retrieve raw HTML content without conversion using the
rawparameter.Content Truncation: Limit the amount of content returned by specifying a
max_length.Chunked Reading: Process large web pages in manageable chunks by specifying a
start_indexto begin content extraction from.
This enables access to up-to-date information directly from web pages.
Converts HTML content from web pages to Markdown format for easier consumption by LLMs
Click on "Deploy 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 Serverfetch the latest tech news from Hacker News"
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.
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-fetchAvailable 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.
ParametersJSON Schema
| 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
A3.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description partially covers behavior: it mentions fetching and markdown extraction but omits error handling, rate limits, or response truncation details. The meta note about past limitations does not add behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief with two sentences, front-loading the main action. The second sentence is slightly verbose but still concise overall.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no siblings or output schema, the description covers the core functionality and parameter usage adequately. It lacks mention of error scenarios but is sufficient for a fetch tool with well-defined schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the description adds minimal value beyond parameter descriptions. It provides context by linking 'optionally extracts markdown' to the 'raw' parameter, but not enough to raise the score above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a URL from the internet and optionally extracts markdown. It distinguishes this from any other tool by emphasizing internet access, and there are no siblings to differentiate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells the agent to use this tool for obtaining up-to-date internet information. Though lacking explicit when-not-to-use, the context of granting internet access implies its primary use case.
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.
1 tool update
- First observed
fetch
TDQS
B3.1/5.0
Scored across 1 tool
Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool has a single, clearly defined purpose of fetching URLs and optionally extracting markdown content.
Naming Consistency5/5
Since there is only one tool, naming consistency is inherently perfect. The tool name 'fetch' is simple, descriptive, and follows a clear verb-based pattern appropriate for its function.
Tool Count2/5
A single tool is too few for a server named 'Fetch MCP Server', which suggests a broader scope for fetching operations. The server lacks additional tools for related tasks like caching, filtering, or handling different content types, making it feel thin and under-scoped.
Completeness2/5
The tool surface is severely incomplete for a fetching domain. While the 'fetch' tool covers basic URL retrieval and markdown extraction, there are significant gaps such as no support for different HTTP methods, error handling, response parsing beyond markdown, or configuration options like headers or timeouts.
Maintenance
ActivityInactive
ResponsivenessUnresponsive
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