MCP Web Research Server
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 Serversearch for 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
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
License
MIT
Author
Available Tools
3 toolssearch_googleC
Search Google for a query
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
TDQS
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 what the tool does but doesn't describe any behavioral traits such as rate limits, authentication needs, result format, pagination, or whether it's a read-only operation. This leaves significant gaps for an agent to understand how to interact with it effectively.
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 with just four words, front-loaded with the core action, and contains zero wasted information. Every word earns its place by directly contributing to understanding the tool's purpose.
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 (a search operation with potential behavioral nuances) and the lack of annotations and output schema, the description is incomplete. It doesn't cover what the tool returns, how results are structured, or any operational constraints, making it insufficient for an agent to use the tool confidently without additional 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 schema description coverage is 100%, with the single parameter 'query' fully documented in the schema. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain query syntax, examples, or constraints), so it meets the baseline for adequate but unenhanced parameter documentation.
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 ('Search') and resource ('Google') with the specific action ('for a query'), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'visit_page' or 'take_screenshot' that might also involve web interactions, so it doesn't reach the highest 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 alternatives like 'visit_page' or 'take_screenshot'. It doesn't mention any prerequisites, exclusions, or specific contexts where this search tool is preferred over direct page visits or other methods.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
take_screenshotB
Take a screenshot of the current page
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
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 action ('take a screenshot') but lacks details on permissions, file format, storage location, or any side effects. This is a significant gap for a tool that performs a capture operation without any structured safety hints.
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 directly states the tool's purpose without any wasted words. It is front-loaded and appropriately sized for a simple tool with no parameters, making it easy for an agent to parse 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the screenshot returns (e.g., image data, file path) or any behavioral nuances, which is inadequate for a tool that performs a capture action without structured 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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds no parameter details, as there are none to explain, aligning with the baseline for zero parameters.
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 ('take') and resource ('screenshot of the current page'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'search_google' or 'visit_page', which are distinct browsing actions rather than screenshot operations, so it lacks explicit sibling differentiation.
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 or in what context it should be invoked. It implies usage for capturing the current page but offers no exclusions, prerequisites, or comparisons to other tools, leaving the agent to infer usage based on the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
visit_pageC
Visit a webpage and extract its content
| 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'extract content' but doesn't specify what content is extracted (e.g., HTML, text, metadata), how errors are handled, rate limits, authentication needs, or whether the operation is idempotent. The description is too vague about the tool's actual behavior beyond the basic action.
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 at just 7 words, front-loading the core functionality with zero wasted language. Every word earns its place by communicating essential information about the tool's action and target.
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 tool with no annotations, no output schema, and behavioral complexity (web scraping can involve many edge cases), the description is incomplete. It doesn't address what content is extracted, error handling, performance characteristics, or relationship to sibling tools. The agent would need to guess about important operational aspects.
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%, so the schema already documents both parameters thoroughly. The description doesn't add any meaningful context about parameter usage beyond what's in the schema (e.g., when to use takeScreenshot, URL format requirements). 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 specific verbs ('visit' and 'extract') and identifies the resource ('webpage content'). It distinguishes itself from sibling 'take_screenshot' by focusing on content extraction rather than just visual capture, though it doesn't explicitly differentiate from 'search_google' 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 'search_google' or 'take_screenshot'. It doesn't mention prerequisites, constraints, or typical use cases, leaving the agent to infer usage from the tool name and parameters alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool has a clearly distinct purpose with no overlap: search_google finds web pages, visit_page loads and extracts content from a specific URL, and take_screenshot captures visual data from the current page. An agent can easily differentiate these functions without confusion.
All tool names follow a consistent verb_noun pattern using snake_case: search_google, take_screenshot, and visit_page. This uniformity makes the set predictable and easy to understand at a glance.
With only 3 tools, the server feels thin for a 'Web Research Server' scope, as it lacks operations like navigating pages, interacting with elements, or managing browser sessions. While the tools cover basic functions, the count is borderline low for comprehensive web research tasks.
There are significant gaps in the tool surface for web research: no navigation tools (e.g., go_back, click_element), no interaction capabilities (e.g., fill_form, scroll), and no session management. This incomplete coverage will likely cause agent failures in complex research workflows.
Maintenance
Resources
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Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Web research for agents: quality-scored Google search, webpage extraction, and deep research.
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
Real-time web search, reasoning, and research through Perplexity's API
Web search and page-reading for AI agents. One-click OAuth connect, or a Caesar API key.
Related MCP Servers
- AlicenseBqualityFmaintenanceA server facilitating web search functionality by utilizing Perplexity AI's API, designed to integrate with the Claude desktop client for enhanced search queries.1308MIT
- AlicenseBqualityDmaintenanceThe MCP Web Research Server enables real-time web research with Claude by integrating Google search, capturing webpage content and screenshots, and tracking research sessions.31786MIT
- AlicenseBqualityDmaintenanceA Model Context Protocol server that enables Claude to perform web research by integrating Google search, extracting webpage content, and capturing screenshots.131,56720MIT
- AlicenseAqualityCmaintenanceA Model Context Protocol server that enables Claude to perform web research by integrating Google search, extracting webpage content, and capturing screenshots in real-time.41,5679MIT
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