Modal (Serverless AI Infrastructure) MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure). "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Modal (Serverless AI Infrastructure)?"
)
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 Modal (Serverless AI Infrastructure) MCP Server
Connect your Modal account to any AI agent and take full control of your high-performance AI infrastructure, serverless GPU deployments, and persistent storage through natural conversation.
LlamaIndex agents combine Modal (Serverless AI Infrastructure) tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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
- App Orchestration — List isolated active and historical Modal app contexts to track function execution states and resource allocation directly from your agent
- Deployment Management — Enumerate promoted long-running deployments and retrieve detailed web endpoints and serving configurations securely
- Operational Control — Force stop actively running Modal app executions gracefully via App ID to prevent unnecessary billing cycles and manage system resources natively
- Security & Secret Audit — List stored secret dictionary references and verify environment variable mappings attached to your serverless functions securely
- Storage Visibility — Monitor persisted disk network block volumes and data mount directories used across your distributed compute instances
- Infrastructure Inspection — Deep-dive into specific App or Deployment IDs to retrieve precise JSON metadata representing your infrastructure's current state vectors
The Modal (Serverless AI Infrastructure) MCP Server exposes 7 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 Modal (Serverless AI Infrastructure) to LlamaIndex via MCP
Follow these steps to integrate the Modal (Serverless AI Infrastructure) 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 7 tools from Modal (Serverless AI Infrastructure)
Why Use LlamaIndex with the Modal (Serverless AI Infrastructure) MCP Server
LlamaIndex provides unique advantages when paired with Modal (Serverless AI Infrastructure) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Modal (Serverless AI Infrastructure) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Modal (Serverless AI Infrastructure) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Modal (Serverless AI Infrastructure), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Modal (Serverless AI Infrastructure) tools were called, what data was returned, and how it influenced the final answer
Modal (Serverless AI Infrastructure) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Modal (Serverless AI Infrastructure) MCP Server delivers measurable value.
Hybrid search: combine Modal (Serverless AI Infrastructure) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) for fresh data
Analytical workflows: chain Modal (Serverless AI Infrastructure) queries with LlamaIndex's data connectors to build multi-source analytical reports
Modal (Serverless AI Infrastructure) MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Modal (Serverless AI Infrastructure) to LlamaIndex via MCP:
get_app
Get static specifics of an exact Modal App ID
get_deployment
Get an explicitly tracked deployment detail mapped bound
list_apps
List isolated active/historical Modal Apps contexts
list_deployments
List strictly managed Modal platform explicitly promoted deployments
list_secrets
List static secret dictionary configuration references
list_volumes
List Modal persisted disk network block volumes
stop_app
Force stop an actively running explicit Modal App execution
Example Prompts for Modal (Serverless AI Infrastructure) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Modal (Serverless AI Infrastructure) immediately.
"List all active Modal apps running in my account"
"Force stop Modal app ID 'ap-123'"
"Show me all persistent volumes configured in my workspace"
Troubleshooting Modal (Serverless AI Infrastructure) MCP Server with LlamaIndex
Common issues when connecting Modal (Serverless AI Infrastructure) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpModal (Serverless AI Infrastructure) + LlamaIndex FAQ
Common questions about integrating Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) 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 Modal (Serverless AI Infrastructure) to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.