E2B 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 E2B 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 E2B. "
"You have 3 tools available."
),
)
response = await agent.run(
"What tools are available in E2B?"
)
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 E2B MCP Server
Connect your AI agent to E2B — the leading sandbox platform for AI code execution, trusted by OpenAI, Anthropic, and thousands of AI companies.
LlamaIndex agents combine E2B 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
- Create Sandboxes — Spin up isolated Linux environments in ~150ms. Each sandbox is a Firecracker microVM with its own kernel, filesystem, and network
- List Sandboxes — Monitor all active sandbox environments, their templates, and resource usage
- Kill Sandboxes — Terminate environments when done to release resources and reduce costs
The E2B 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 E2B to LlamaIndex via MCP
Follow these steps to integrate the E2B 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 E2B
Why Use LlamaIndex with the E2B MCP Server
LlamaIndex provides unique advantages when paired with E2B through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine E2B tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain E2B tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query E2B, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what E2B tools were called, what data was returned, and how it influenced the final answer
E2B + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the E2B MCP Server delivers measurable value.
Hybrid search: combine E2B real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query E2B 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 E2B for fresh data
Analytical workflows: chain E2B queries with LlamaIndex's data connectors to build multi-source analytical reports
E2B MCP Tools for LlamaIndex (3)
These 3 tools become available when you connect E2B to LlamaIndex via MCP:
create_sandbox
The sandbox is an isolated Linux VM that starts in ~150ms. Use templates like "base" (default), "python3", or "node" for pre-configured environments. Default timeout is 300 seconds. Create a new isolated cloud sandbox for running code securely. Each sandbox is a Firecracker microVM with its own filesystem
kill_sandbox
The sandbox and its filesystem contents are permanently deleted. Terminate a running sandbox by its ID
list_sandboxes
Useful for monitoring active environments and managing resources. List all currently active sandboxes in your E2B account
Example Prompts for E2B in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with E2B immediately.
"Create a Python sandbox so I can run a data analysis script."
"Show me all my running sandboxes."
"Kill sandbox sbx_ghi789 — I'm done with it."
Troubleshooting E2B MCP Server with LlamaIndex
Common issues when connecting E2B to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpE2B + LlamaIndex FAQ
Common questions about integrating E2B 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 E2B 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 E2B to LlamaIndex
Get your token, paste the configuration, and start using 3 tools in under 2 minutes. No API key management needed.