Upstash Redis 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 Upstash Redis as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Upstash Redis. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Upstash Redis?"
)
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 Upstash Redis MCP Server
Connect your Upstash Redis serverless database securely to your conversational AI agent via their REST API. Activating this integration grants your AI the technical autonomy to function as a responsive database administrator, enabling it to scan live keys, read raw datastore strings, set temporal expiration values, and even debug in real-time straight from your chat or IDE terminal.
LlamaIndex agents combine Upstash Redis tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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
- Read & Write Values — Fetch the exact string configuration of a stored key (
get), or instruct the AI to inject a new value (set) complete with Time-To-Live (TTL) expiration limits. - Data Structure Discovery — Perform pattern-based scans across your database to track down dynamically generated keys and inspect their underlying structures or lifespans (
list_keys,get_key_info). - Manage Counters — Safely increment or decrement numerical keys dynamically, perfect for managing rate limits, operational counters, or user session metrics directly during troubleshooting flows.
- Maintain Health — Ping the cluster instance for responsive status checks and remove (
delete) specific keys or cache fragments completely.
The Upstash Redis 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 Upstash Redis to LlamaIndex via MCP
Follow these steps to integrate the Upstash Redis 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 Upstash Redis
Why Use LlamaIndex with the Upstash Redis MCP Server
LlamaIndex provides unique advantages when paired with Upstash Redis through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Upstash Redis tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Upstash Redis tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Upstash Redis, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Upstash Redis tools were called, what data was returned, and how it influenced the final answer
Upstash Redis + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Upstash Redis MCP Server delivers measurable value.
Hybrid search: combine Upstash Redis real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Upstash Redis 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 Upstash Redis for fresh data
Analytical workflows: chain Upstash Redis queries with LlamaIndex's data connectors to build multi-source analytical reports
Upstash Redis MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Upstash Redis to LlamaIndex via MCP:
delete
Provide a comma-separated list of keys. Deletes one or more keys from Redis
get
Retrieves the string value stored at a key
get_key_info
Retrieves the data type and TTL of a key
increment
Use negative numbers to decrement. Increments or decrements a numeric counter at a key
list_keys
Avoid broad patterns like "*" on large databases. Scans for keys matching a glob pattern
ping
Pings the Redis instance to verify connectivity
set
You can specify expiry in seconds. Sets a string value at a key with an optional TTL
Example Prompts for Upstash Redis in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Upstash Redis immediately.
"List all active Redis keys associated with app sessions."
"Check the Time-To-Live duration limit configured for the 'cache:product_header' key."
"Delete all caching strings tagged as 'user_193_avatar' from the database immediately."
Troubleshooting Upstash Redis MCP Server with LlamaIndex
Common issues when connecting Upstash Redis to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpUpstash Redis + LlamaIndex FAQ
Common questions about integrating Upstash Redis 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 Upstash Redis 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 Upstash Redis to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.