ScreenshotOne MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ScreenshotOne as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to ScreenshotOne. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in ScreenshotOne?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About ScreenshotOne MCP Server
Empower your AI agent to orchestrate your entire visual auditing and website capturing workflow with ScreenshotOne, the reliable API for high-quality screenshots. By connecting ScreenshotOne to your agent, you transform complex capturing tasks into a natural conversation. Your agent can instantly take full-page screenshots, generate PDFs from URLs, and audit page metadata without you ever touching a browser. Whether you are monitoring site changes or archiving visual content, your agent acts as a real-time visual archiver, ensuring your records are always clear and comprehensive.
LlamaIndex agents combine ScreenshotOne tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Visual Auditing — Take high-resolution screenshots of any URL and retrieve the direct image links instantly.
- Full-page Oversight — Capture entire website pages from top to bottom to maintain a complete view of site design and content.
- Document Intelligence — Generate professional PDFs from any website for easy sharing and offline auditing.
- Metadata Discovery — Retrieve website titles, descriptions, and page sizes without performing a full capture.
- Targeted Capturing — Take screenshots of specific CSS elements or custom viewports to focus on what matters most.
The ScreenshotOne MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect ScreenshotOne to LlamaIndex via MCP
Follow these steps to integrate the ScreenshotOne MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from ScreenshotOne
Why Use LlamaIndex with the ScreenshotOne MCP Server
LlamaIndex provides unique advantages when paired with ScreenshotOne through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ScreenshotOne tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ScreenshotOne tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ScreenshotOne, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ScreenshotOne tools were called, what data was returned, and how it influenced the final answer
ScreenshotOne + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ScreenshotOne MCP Server delivers measurable value.
Hybrid search: combine ScreenshotOne real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ScreenshotOne to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying ScreenshotOne for fresh data
Analytical workflows: chain ScreenshotOne queries with LlamaIndex's data connectors to build multi-source analytical reports
ScreenshotOne MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect ScreenshotOne to LlamaIndex via MCP:
generate_pdf
Generate a PDF from a URL
get_page_metadata
) without capturing. Get metadata for a website (title, size, etc.) without capturing
take_element_screenshot
Take a screenshot of a specific CSS element
take_full_page_screenshot
Take a full-page screenshot of a URL
take_screenshot
Take a screenshot of a URL
take_viewport_screenshot
Take a screenshot with a specific viewport size
Example Prompts for ScreenshotOne in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ScreenshotOne immediately.
"Take a screenshot of https://vinkius.com."
"Capture the full page of https://github.com/vinkius."
"Show metadata for https://www.google.com."
Troubleshooting ScreenshotOne MCP Server with LlamaIndex
Common issues when connecting ScreenshotOne to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpScreenshotOne + LlamaIndex FAQ
Common questions about integrating ScreenshotOne MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect ScreenshotOne with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ScreenshotOne to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.