Baseten 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 Baseten 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 Baseten. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Baseten?"
)
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 Baseten MCP Server
Connect your Baseten account to any AI agent and track, deploy, and execute your machine learning models through natural conversation.
LlamaIndex agents combine Baseten tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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.
O que você pode fazer
- Model Management — List managed models, fetch configurations, and understand active routing boundaries
- Serverless Deployments — Inspect exact replica states, autoscaling configurations, and deployment versions
- Inference Execution — Run direct predictions (
predict) pushing tensor payloads or JSON directly to GPU weights - Workspace Secrets — Enumerate active environment secrets securely mapped inside the isolated orchestration ecosystem
Como funciona
1. Subscribe to this server
2. Enter your Baseten API Key
3. Gain complete ML-Ops control over your active inference nodes using Claude, Cursor, or your preferred agent
Scale unified AI infrastructure without bouncing between terminal windows. Your agent becomes a capable Machine Learning Operator tracking your GPU lifecycle.
Para quem é?
- ML Engineers — execute test payloads to deployments instantaneously without spinning up local Python notebooks
- DevOps/SREs — audit running deployment resources and verify replica states reliably from your core IDE
- AI Researchers — inspect version schemas and manage inference pipeline architectures quickly
The Baseten 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 Baseten to LlamaIndex via MCP
Follow these steps to integrate the Baseten 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 Baseten
Why Use LlamaIndex with the Baseten MCP Server
LlamaIndex provides unique advantages when paired with Baseten through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Baseten tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Baseten tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Baseten, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Baseten tools were called, what data was returned, and how it influenced the final answer
Baseten + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Baseten MCP Server delivers measurable value.
Hybrid search: combine Baseten real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Baseten 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 Baseten for fresh data
Analytical workflows: chain Baseten queries with LlamaIndex's data connectors to build multi-source analytical reports
Baseten MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Baseten to LlamaIndex via MCP:
get_deployment
Get explicit details of a running deployment
get_model
Get a specific Baseten model
list_deployments
List active inferences bounds matching a specific model
list_models
List Baseten managed models
list_secrets
List securely managed workspace secrets without showing values
predict
Formulate the explicit tensor shapes or dictionaries strictly matching the deployed instance. Invoke a serverless model inference prediction
Example Prompts for Baseten in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Baseten immediately.
"List standard machine learning models we currently host on Baseten."
"Run a prediction against the Sentiment model ID 12345 using this text input: 'The new feature completely broke my workflow.'"
"Check if our Baseten project has a secret scoped as 'OPENAI_API_KEY_FALLBACK'."
Troubleshooting Baseten MCP Server with LlamaIndex
Common issues when connecting Baseten to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBaseten + LlamaIndex FAQ
Common questions about integrating Baseten 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 Baseten 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 Baseten to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.