The Cloudflare Browser Rendering MCP server fetches, processes, and summarizes web content for use in LLMs (Large Language Models).
Fetch and process web pages: Extract content from URLs for LLM context
Search Cloudflare documentation: Retrieve relevant content based on search queries
Extract structured content: Use CSS selectors to pull specific elements from web pages
Summarize web content: Condense page content into concise summaries
Take screenshots: Capture screenshots of web pages with customizable options
Uses Cloudflare Browser Rendering API to fetch and process web pages, search Cloudflare documentation, and capture screenshots
Requires deployment of a Cloudflare Worker with Browser Rendering binding and KV namespace for screenshots functionality
Leverages Puppeteer functionality through Cloudflare's Browser Rendering API for web content fetching and screenshot capture
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., "@cloudflare-browser-rendering-mcp fetch and summarize the latest Cloudflare blog post about browser rendering"
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.
This MCP (Model Context Protocol) server provides tools for fetching and processing web content using Cloudflare Browser Rendering for use as context in LLMs. It's designed to work with both Claude and Cline client environments.
Web Content Fetching: Fetch and process web pages for LLM context
Documentation Search: Search Cloudflare documentation and return relevant content
Structured Content Extraction: Extract structured content from web pages using CSS selectors
Content Summarization: Summarize web content for more concise LLM context
Screenshot Capture: Take screenshots of web pages
Related MCP server: turbowebfetch
Node.js v18 or higher
A Cloudflare account with Browser Rendering API access
A deployed Cloudflare Worker using the provided puppeteer-worker.js file
To install Cloudflare Browser Rendering for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @amotivv/cloudflare-browser-rendering-mcp --client claudeClone this repository:
git clone https://github.com/yourusername/cloudflare-browser-rendering.git
cd cloudflare-browser-renderingInstall dependencies:
npm installBuild the project:
npm run buildDeploy the puppeteer-worker.js file to Cloudflare Workers using Wrangler:
npx wrangler deployMake sure to configure the following bindings in your Cloudflare Worker:
Browser Rendering binding named browser
KV namespace binding named SCREENSHOTS
Note the URL of your deployed worker (e.g., https://browser-rendering-api.yourusername.workers.dev)
Open the Claude Desktop configuration file:
# macOS
code ~/Library/Application\ Support/Claude/claude_desktop_config.json
# Windows
code %APPDATA%\Claude\claude_desktop_config.jsonAdd the MCP server configuration:
{
"mcpServers": {
"cloudflare-browser-rendering": {
"command": "node",
"args": ["/path/to/cloudflare-browser-rendering/dist/index.js"],
"env": {
"BROWSER_RENDERING_API": "https://your-worker-url.workers.dev"
},
"disabled": false,
"autoApprove": []
}
}
}Restart Claude Desktop
Open the Cline MCP settings file:
# macOS
code ~/Library/Application\ Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
# Windows
code %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.jsonAdd the MCP server configuration:
{
"mcpServers": {
"cloudflare-browser-rendering": {
"command": "node",
"args": ["/path/to/cloudflare-browser-rendering/dist/index.js"],
"env": {
"BROWSER_RENDERING_API": "https://your-worker-url.workers.dev"
},
"disabled": false,
"autoApprove": []
}
}
}Once configured, the MCP server will be available to both Claude Desktop and Cline. You can use the following tools:
Fetches and processes a web page for LLM context.
Parameters:
url (required): URL to fetch
maxContentLength (optional): Maximum content length to return
Example:
Can you fetch and summarize the content from https://developers.cloudflare.com/browser-rendering/?Searches Cloudflare documentation and returns relevant content.
Parameters:
query (required): Search query
maxResults (optional): Maximum number of results to return
Example:
Search the Cloudflare documentation for information about "browser rendering API".Extracts structured content from a web page using CSS selectors.
Parameters:
url (required): URL to extract content from
selectors (required): CSS selectors to extract content
Example:
Extract the main heading and first paragraph from https://developers.cloudflare.com/browser-rendering/ using the selectors h1 and p.Summarizes web content for more concise LLM context.
Parameters:
url (required): URL to summarize
maxLength (optional): Maximum length of the summary
Example:
Summarize the content from https://developers.cloudflare.com/browser-rendering/ in 300 words or less.Takes a screenshot of a web page.
Parameters:
url (required): URL to take a screenshot of
width (optional): Width of the viewport in pixels (default: 1280)
height (optional): Height of the viewport in pixels (default: 800)
fullPage (optional): Whether to take a screenshot of the full page or just the viewport (default: false)
Example:
Take a screenshot of https://developers.cloudflare.com/browser-rendering/ with a width of 1024 pixels.The MCP server uses comprehensive logging with the following prefixes:
[Setup]: Initialization and configuration
[API]: API requests and responses
[Error]: Error handling and debugging
To view logs:
Claude Desktop: Check the logs in ~/Library/Logs/Claude/mcp*.log (macOS) or %APPDATA%\Claude\Logs\mcp*.log (Windows)
Cline: Logs appear in the output console of the VSCode extension
"BROWSER_RENDERING_API environment variable is not set"
Make sure you've set the correct URL to your Cloudflare Worker in the MCP server configuration
"Cloudflare worker API is unavailable or not configured"
Verify that your Cloudflare Worker is deployed and running
Check that the URL is correct and accessible
"Browser binding is not available"
Ensure that you've configured the Browser Rendering binding in your Cloudflare Worker
"SCREENSHOTS KV binding is not available"
Ensure that you've configured the KV namespace binding in your Cloudflare Worker
src/index.ts: Main entry point
src/server.ts: MCP server implementation
src/browser-client.ts: Client for interacting with Cloudflare Browser Rendering
src/content-processor.ts: Processes web content for LLM context
puppeteer-worker.js: Cloudflare Worker implementation
npm run buildThe project includes a comprehensive test script that verifies all MCP tools are working correctly:
npm testThis will:
Start the MCP server
Test each tool with sample requests
Verify the responses
Provide a summary of test results
You can also run individual tests for specific components:
# Test the Puppeteer integration
npm run test:puppeteerFor the tests to work properly, make sure you have:
Built the project with npm run build
Set the BROWSER_RENDERING_API environment variable to your Cloudflare Worker URL
Deployed the Cloudflare Worker with the necessary bindings
MIT
Extracts structured content from a web page using CSS selectors
| Name | Required | Description | Default |
|---|---|---|---|
| selectors | Yes | CSS selectors to extract content | |
| url | Yes | URL to extract 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 states what the tool does but lacks critical behavioral details: it doesn't specify if it fetches the page internally, handles errors, requires internet access, has rate limits, or what the output format is. For a tool with no annotations, this leaves significant gaps in understanding its operation.
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 and front-loaded: a single sentence that directly states the tool's function without any fluff. Every word earns its place by conveying essential information about extraction, content type, source, and method. It's efficiently structured for quick understanding.
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 web extraction (involving network calls, parsing, and structured data output), the description is incomplete. There's no output schema, and the description doesn't explain return values, error handling, or behavioral traits. With no annotations and only basic parameter coverage, it fails to provide enough context for effective use in real-world scenarios.
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 schema description coverage is 100%, meaning the input schema already documents both parameters ('url' and 'selectors') with descriptions. The tool description adds no additional meaning beyond what's in the schema—it doesn't explain parameter interactions, provide examples, or clarify semantics. With high schema coverage, the baseline score of 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?
The description clearly states the tool's purpose: 'Extracts structured content from a web page using CSS selectors'. It specifies the verb ('extracts'), resource ('structured content'), and method ('CSS selectors'), which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'fetch_page' or 'summarize_content', which would require a 5.
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 alternatives. It doesn't mention scenarios where extraction is preferred over fetching the whole page, searching documentation, summarizing, or taking a screenshot. Without any context or exclusions, users must infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetches and processes a web page for LLM context
| Name | Required | Description | Default |
|---|---|---|---|
| maxContentLength | No | Maximum content length to return | |
| url | Yes | URL to fetch |
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 'fetches and processes' but doesn't specify what processing entails (e.g., cleaning HTML, extracting text, handling errors), whether it requires authentication, rate limits, or what happens with invalid URLs. 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 purpose without unnecessary details. Every word earns its place, making it easy for an agent to quickly grasp the tool's function.
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 web fetching (external calls, potential errors) and lack of annotations or output schema, the description is insufficient. It doesn't explain return values, error handling, or processing behavior, leaving the agent with incomplete information for reliable tool invocation 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 'maxContentLength'). The description adds no additional parameter semantics beyond what the schema provides, such as format details for URLs or units for content length. Baseline 3 is appropriate since the schema does the heavy lifting.
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 ('fetches and processes') and resource ('a web page'), with the purpose being to provide 'LLM context'. It distinguishes from siblings like 'take_screenshot' (visual capture) and 'summarize_content' (summarization), though it doesn't explicitly differentiate from 'extract_structured_content' or 'search_documentation' which might have overlapping functionality.
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 is provided on when to use this tool versus alternatives like 'extract_structured_content' or 'search_documentation'. The description implies usage for web page retrieval for LLM context, but lacks explicit when/when-not instructions or prerequisites, leaving the agent to infer based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Searches Cloudflare documentation and returns relevant content
| Name | Required | Description | Default |
|---|---|---|---|
| maxResults | No | Maximum number of results to return | |
| query | Yes | Search query |
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 states the tool searches and returns content, but doesn't describe important behaviors like whether it performs web searches, accesses a local database, requires authentication, has rate limits, or what format the returned content takes (e.g., text snippets, links, full documents).
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 gets straight to the point without unnecessary words. It's appropriately sized for a simple search tool, though it could potentially be more structured with additional context.
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 search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what kind of content is returned (snippets, full pages, metadata), how results are ranked, whether authentication is needed, or any limitations. Given the lack of structured fields, the description should provide more operational context.
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 ('query' and 'maxResults'). The description doesn't add any meaningful parameter semantics beyond what the schema already provides, such as search syntax examples or result format details.
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 ('Searches') and resource ('Cloudflare documentation'), making it immediately understandable. However, it doesn't distinguish this tool from its sibling tools like 'fetch_page' or 'extract_structured_content', which might also retrieve documentation content in different ways.
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 alternatives. It doesn't mention any prerequisites, constraints, or compare it to sibling tools like 'fetch_page' (which might retrieve a specific page) or 'summarize_content' (which might process content).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Summarizes web content for more concise LLM context
| Name | Required | Description | Default |
|---|---|---|---|
| maxLength | No | Maximum length of the summary | |
| url | Yes | URL to summarize |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'summarizes web content' but doesn't describe how it works (e.g., extraction method, processing time, error handling), what limitations exist (e.g., supported content types, rate limits), or what the output looks like. 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 with zero waste. It's front-loaded with the core purpose and includes a clear goal, making it appropriately sized and well-structured for quick understanding.
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 summarizing web content, no annotations, and no output schema, the description is incomplete. It doesn't explain the return format, potential errors, or behavioral traits like content processing methods. For a tool with 2 parameters and significant operational implications, more context is needed.
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 'maxLength'). The description adds no additional parameter semantics beyond what the schema provides, such as format details for 'url' or typical values for 'maxLength'. Baseline 3 is appropriate when the schema does the heavy lifting.
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 ('summarizes') and resource ('web content'), and it provides the goal ('for more concise LLM context'). However, it doesn't explicitly differentiate from sibling tools like 'extract_structured_content' or 'fetch_page', which might have overlapping functionality.
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 alternatives like 'extract_structured_content' or 'fetch_page'. It doesn't mention prerequisites, exclusions, or specific contexts where this summarization tool is preferred over other content-handling siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Takes a screenshot of a web page and returns it as an image
| Name | Required | Description | Default |
|---|---|---|---|
| fullPage | No | Whether to take a screenshot of the full page or just the viewport (default: false) | |
| height | No | Height of the viewport in pixels (default: 800) | |
| url | Yes | URL to take a screenshot of | |
| width | No | Width of the viewport in pixels (default: 1280) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden but lacks behavioral details. It doesn't disclose potential issues like authentication needs for restricted pages, rate limits, performance impacts, or what happens with invalid URLs. The phrase 'returns it as an image' hints at output but doesn't specify format (e.g., PNG, JPEG) or handling of errors.
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 purpose. Every word earns its place, with no redundant or vague phrasing. It's appropriately sized for a straightforward tool.
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 (capturing web pages with 4 parameters) and lack of annotations or output schema, the description is incomplete. It doesn't cover error cases, output format details, or prerequisites (e.g., network access). For a tool that interacts with external resources and returns binary data, more context is needed.
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 description adds no parameter-specific information beyond what's in the schema, which has 100% coverage. It doesn't explain interactions between parameters (e.g., how 'fullPage' affects 'height'/'width') or provide usage examples. Since schema coverage is high, the baseline is 3, but no extra value is added.
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 ('takes a screenshot') and resource ('of a web page'), with the specific output format ('returns it as an image'). It distinguishes from sibling tools like 'fetch_page' (which likely retrieves HTML) and 'extract_structured_content' (which processes content rather than capturing visuals).
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 is provided on when to use this tool versus alternatives. It doesn't mention scenarios like needing visual verification, capturing dynamic content, or comparing with text-based tools like 'summarize_content' or 'fetch_page'. The description only states what it does, not when it's appropriate.
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.
v1.0.0extract_structured_contentfetch_pagesearch_documentationsummarize_contenttake_screenshotEach tool has a clearly distinct purpose with no overlap: extract_structured_content targets specific elements, fetch_page retrieves full pages, search_documentation queries documentation, summarize_content condenses content, and take_screenshot captures visual output. An agent can easily differentiate between these functions.
All tools follow a consistent verb_noun pattern with snake_case naming (e.g., extract_structured_content, fetch_page, search_documentation). This uniformity makes the toolset predictable and easy to understand for an agent.
With 5 tools, this server is well-scoped for browser rendering and content processing tasks. Each tool earns its place by covering distinct aspects like fetching, extracting, searching, summarizing, and screenshotting, without being overly sparse or bloated.
The toolset covers core browser rendering workflows (fetching, extracting, summarizing, screenshotting) and includes a domain-specific search function. A minor gap exists in advanced interactions like form submission or navigation, but agents can work around this for most use cases.
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