MCP Web Research Server
Enables Google search integration to retrieve search results and extract information from the web
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 Web Research Serverfind recent developments in quantum computing"
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.
MCP Web Research Server
A Model Context Protocol (MCP) server for web research.
Bring real-time info into Claude and easily research any topic.
Features
Google search integration --- THIS FORK FIXES THIS --- NOW NO LONGER GETTING CAPTCHA BLOCKED
Webpage content extraction
Research session tracking (list of visited pages, search queries, etc.)
Screenshot capture
Related MCP server: MCP Web Research Server
Prerequisites
Node.js >= 18 (includes
npmandnpx)
Installation
First, ensure you've downloaded and installed the Claude Desktop app and you have npm installed.
Next, add this entry to your claude_desktop_config.json (on Mac, found at ~/Library/Application\ Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"webresearch": {
"command": "npx",
"args": ["-y", "@mzxrai/mcp-webresearch@latest"]
}
}
}This config allows Claude Desktop to automatically start the web research MCP server when needed.
Usage
Simply start a chat with Claude and send a prompt that would benefit from web research. If you'd like a prebuilt prompt customized for deeper web research, you can use the agentic-research prompt that we provide through this package. Access that prompt in Claude Desktop by clicking the Paperclip icon in the chat input and then selecting Choose an integration → webresearch → agentic-research.
Tools
search_googlePerforms Google searches and extracts results
Arguments:
{ query: string }
visit_pageVisits a webpage and extracts its content
Arguments:
{ url: string, takeScreenshot?: boolean }
take_screenshotTakes a screenshot of the current page
No arguments required
Prompts
agentic-research
A guided research prompt that helps Claude conduct thorough web research. The prompt instructs Claude to:
Start with broad searches to understand the topic landscape
Prioritize high-quality, authoritative sources
Iteratively refine the research direction based on findings
Keep you informed and let you guide the research interactively
Always cite sources with URLs
Resources
We expose two things as MCP resources: (1) captured webpage screenshots, and (2) the research session.
Screenshots
When you take a screenshot, it's saved as an MCP resource. You can access captured screenshots in Claude Desktop via the Paperclip icon.
Research Session
The server maintains a research session that includes:
Search queries
Visited pages
Extracted content
Screenshots
Timestamps
Suggestions
For the best results, if you choose not to use the agentic-research prompt when doing your research, it may be helpful to suggest high-quality sources for Claude to use when researching general topics. For example, you could prompt news today from reuters or AP instead of news today.
Problems
This is very much pre-alpha code. And it is also AIGC, so expect bugs.
If you run into issues, it may be helpful to check Claude Desktop's MCP logs:
tail -n 20 -f ~/Library/Logs/Claude/mcp*.logDevelopment
# Install dependencies
pnpm install
# Build the project
pnpm build
# Watch for changes
pnpm watch
# Run in development mode
pnpm devRequirements
Node.js >= 18
Playwright (automatically installed as a dependency)
Verified Platforms
macOS
Linux
Windows
License
MIT
Author
Available Tools
4 toolssearch_googleA
Performs a web search using Google, ideal for finding current information, news, websites, and general knowledge. Use this tool when you need to research topics, find recent information, or gather data from the web. Returns structured search results with titles, URLs, and snippets.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral aspects. States return format (titles, URLs, snippets) but omits details like rate limits, query length limits, pagination, or error handling. Basic transparency but with notable gaps.
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?
Three sentences, no unnecessary words, front-loaded with purpose. Every sentence adds value: what it does, when to use, what it returns.
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 a simple tool with one parameter and no output schema, the description covers primary purpose, use cases, and return format. Lacks details like result count limits or error scenarios, but adequate for basic usage.
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?
Only one parameter (query) with 100% schema description coverage (minimal: 'Search query'). The tool description adds no further parameter-specific meaning, only repeating use cases. 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?
Clearly states 'Performs a web search using Google' with specific use cases (current information, news, websites, general knowledge). Distinguishes from sibling tools like search_scholar (academic) and visit_page (browsing).
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?
Provides clear guidance on when to use: 'research topics, find recent information, or gather data from the web.' Does not explicitly mention when not to use or alternatives, but context implies a general web search tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_scholarA
Searches Google Scholar for academic papers and scholarly articles. Use this tool when researching scientific topics, looking for peer-reviewed research, academic citations, or scholarly literature. Returns structured data including titles, authors, publication details, and citation counts. Ideal for academic research and evidence-based inquiries.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Academic search query |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description should disclose behavioral traits. It only states that it returns structured data with certain fields, but does not mention whether the tool is read-only, has rate limits, requires authentication, or any side effects. This lack of transparency is a gap.
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 concise with three sentences, front-loading the core action. It is efficient and easy to parse, though slightly more structure (e.g., bullet points) could improve readability.
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 simple search tool with one parameter and no output schema, the description covers purpose, usage context, and return structure. However, it lacks details on pagination, result limits, or sorting, which could be useful for an 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 already provides a description for the only parameter ('Academic search query'). The tool description does not add any additional meaning beyond what the schema states, so baseline 3 applies.
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 action ('Searches Google Scholar') and specifies the resource ('academic papers and scholarly articles'). It distinguishes itself from sibling tools like search_google by focusing on academic 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 provides explicit guidance on when to use this tool (researching scientific topics, peer-reviewed research, etc.). It does not explicitly mention when not to use or contrast with alternatives, but the focus on academic literature implicitly differentiates it from general web searches.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
take_screenshotA
Captures a visual image of the currently loaded webpage. Use this tool when you need to preserve visual information, analyze page layouts, or document the current state of a webpage. Perfect for situations where textual content alone doesn't convey the full context.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description lacks details about side effects, image format, or behavior if no page is loaded, leaving significant behavioral gaps.
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 two efficient sentences, front-loaded with the verb 'Captures', and every word adds value without redundancy.
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?
While the purpose is clear, the description omits details about output (e.g., format, full-page vs viewport), which would help completeness for a simple tool.
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?
With zero parameters, baseline is 4; the description correctly implies no configuration is needed.
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 captures a visual image of the currently loaded webpage, and it is distinct from sibling tools like search_google, search_scholar, and visit_page.
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 clear use cases (preserve visual info, analyze layouts, document state) but does not explicitly mention when not to use it or alternatives, though siblings are different.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
visit_pageA
Navigates to a specific URL and extracts the page content in readable format, with option to capture a screenshot. Use this tool to deeply analyze specific web pages, read articles, examine documentation, or verify information directly from the source. Especially useful for in-depth research after identifying relevant pages via search.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL to visit | |
| takeScreenshot | No | Whether to take a screenshot |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. It mentions extracting content and screenshots but omits details like error handling, dynamic content, rate limits, or side effects.
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?
Three sentences, first covers core action and options, next two provide guidance. No unnecessary words, well-structured and front-loaded.
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 simple tool with 2 params and no output schema, description adequately covers purpose and usage but lacks details on output format (e.g., how page content is returned) and potential limitations.
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% with adequate descriptions. Description adds context for takeScreenshot ('option to capture a screenshot') but does not clarify URL format or restrictions beyond the schema.
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 (navigates to URL, extracts content, optionally screenshots) and differentiates from sibling tools like search_google (which finds pages) and take_screenshot (which only captures images).
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?
Explicitly recommends use for in-depth research after search, and outlines scenarios (analyze pages, read articles, verify info), providing clear context for when to use.
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.
4 tool updates
- First observed
search_google - First observed
search_scholar - First observed
take_screenshot - First observed
visit_page
TDQS
Each tool has a clearly distinct purpose: general web search vs. academic search vs. page content extraction vs. visual capture. No overlaps in functionality.
All tool names follow a consistent verb_noun pattern with snake_case (search_google, search_scholar, take_screenshot, visit_page), making them predictable.
Four tools is well-scoped for a web research server—enough to cover core tasks without unnecessary complexity.
The set covers search (web and academic), page visiting, and screenshot capabilities. Missing a tool for managing search history or saving results, but core research workflows are supported.
Maintenance
Resources
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