Spider MCP Server for LlamaIndex 3 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Spider as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Spider. "
"You have 3 tools available."
),
)
response = await agent.run(
"What tools are available in Spider?"
)
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 Spider MCP Server
Connect your AI agent to Spider.cloud — the fastest web scraping API in the market, built in Rust for maximum performance.
LlamaIndex agents combine Spider tool responses with indexed documents for comprehensive, grounded answers. Connect 3 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
- Scrape Pages — Extract content from any URL as Markdown, HTML, or plain text. Spider handles JavaScript rendering, anti-bot protection, and proxy rotation
- Crawl Sites — Recursively crawl entire websites at speeds exceeding 100K pages/second. Follow internal links and extract structured data at scale
- Search & Scrape — Search the web and scrape results in a single API call. Combines discovery with extraction for maximum efficiency
Why Spider over alternatives?
- 10-20x faster than Firecrawl for large crawls (Rust engine vs Node.js)
- Lower cost per page at high volume
- Built-in stealth mode with fingerprint rotation and residential proxies
The Spider MCP Server exposes 3 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 Spider to LlamaIndex via MCP
Follow these steps to integrate the Spider 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 3 tools from Spider
Why Use LlamaIndex with the Spider MCP Server
LlamaIndex provides unique advantages when paired with Spider through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Spider tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Spider tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Spider, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Spider tools were called, what data was returned, and how it influenced the final answer
Spider + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Spider MCP Server delivers measurable value.
Hybrid search: combine Spider real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Spider 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 Spider for fresh data
Analytical workflows: chain Spider queries with LlamaIndex's data connectors to build multi-source analytical reports
Spider MCP Tools for LlamaIndex (3)
These 3 tools become available when you connect Spider to LlamaIndex via MCP:
spider_crawl
Spider.cloud Rust engine follows internal links and scrapes each page. Configure depth and page limits to control scope. Crawl an entire website at blazing speed — up to 100K+ pages/second. Returns content from multiple pages following internal links
spider_scrape
cloud Rust-powered engine to scrape a single URL. Handles JavaScript rendering, anti-bot protection, and proxy rotation automatically. Supports multiple output formats: markdown (default), html, text. Scrape a single web page at high speed using Spider.cloud. Returns clean content in Markdown, HTML, or plain text format
spider_search
Combines search + scrape in one API call for maximum efficiency. Search the web and scrape results in a single high-performance request via Spider.cloud
Example Prompts for Spider in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Spider immediately.
"Scrape the homepage of spider.cloud and show me what they offer."
"Crawl docs.python.org and get the first 5 pages."
"Search for 'machine learning frameworks comparison 2026' and scrape the top 3 results."
Troubleshooting Spider MCP Server with LlamaIndex
Common issues when connecting Spider to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSpider + LlamaIndex FAQ
Common questions about integrating Spider 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 Spider 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 Spider to LlamaIndex
Get your token, paste the configuration, and start using 3 tools in under 2 minutes. No API key management needed.