Nimbleway MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Nimbleway as an MCP tool provider through 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 Nimbleway. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Nimbleway?"
)
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 Nimbleway MCP Server
Connect your Nimbleway account to your AI agent and leverage high-performance web data collection through natural conversation.
LlamaIndex agents combine Nimbleway tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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
- Web Extraction — Fetch and render any web page as raw HTML or clean Markdown using advanced stealth technology to bypass bots.
- Structured Search — Execute real-time web searches and receive structured data directly from major search engines.
- Pipeline Management — List and inspect your data streams (pipelines) to monitor your scraping workflows.
- Usage Monitoring — Track your current bandwidth, remaining credits, and overall account usage in real-time.
- Job Tracking — Monitor the progress and metadata of your active data extraction and crawling jobs.
- Proxy Oversight — Access configuration details for your residential and data center proxy endpoints.
The Nimbleway MCP Server exposes 10 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 Nimbleway to LlamaIndex via MCP
Follow these steps to integrate the Nimbleway 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 10 tools from Nimbleway
Why Use LlamaIndex with the Nimbleway MCP Server
LlamaIndex provides unique advantages when paired with Nimbleway through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Nimbleway tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Nimbleway tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Nimbleway, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Nimbleway tools were called, what data was returned, and how it influenced the final answer
Nimbleway + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Nimbleway MCP Server delivers measurable value.
Hybrid search: combine Nimbleway real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Nimbleway 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 Nimbleway for fresh data
Analytical workflows: chain Nimbleway queries with LlamaIndex's data connectors to build multi-source analytical reports
Nimbleway MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Nimbleway to LlamaIndex via MCP:
extract_html
Extract web page as HTML
extract_markdown
Extract web page as Markdown
get_account_usage
Check account bandwidth and usage
get_job
Get specific job details
get_me
Get current account info
get_pipeline
Get specific pipeline details
list_jobs
List scraping jobs
list_pipelines
List scraping pipelines
list_proxies
List proxy configuration
search_web
Perform structured web search
Example Prompts for Nimbleway in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Nimbleway immediately.
"Extract the content of 'https://example.com' as Markdown."
"Search the web for 'latest AI developments 2024' and give me structured results."
"Check my account usage and remaining credits."
Troubleshooting Nimbleway MCP Server with LlamaIndex
Common issues when connecting Nimbleway to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpNimbleway + LlamaIndex FAQ
Common questions about integrating Nimbleway 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 Nimbleway 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 Nimbleway to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.