How to Use the Upstash Redis MCP in LlamaIndex

Q: How do I use Upstash Redis with LlamaIndex for semantic search?

LlamaIndex ingests data from MCP Server tools like get and listkeys. Instead of just showing the raw output, it converts that key-value pair into embeddings, making the information searchable by meaning.

Q: Can I check key expiry when using Upstash Redis with LlamaIndex?

Absolutely. The getkeyinfo tool tells you the TTL (Time To Live) of a key. This helps LlamaIndex know if the data it's indexing is about to become stale, improving answer reliability.

Q: Does Upstash Redis support tracking metrics in LlamaIndex?

Yes. You can use increment to count events or calls, and then index that running total. This lets you query things like 'How many times was this feature used last week?' directly from the knowledge base.

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Connect Upstash Redis MCP to LlamaIndex

Create your Vinkius account to connect Upstash Redis to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Upstash Redis with LlamaIndex

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Claude

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Create a unified, queryable index via the MCP Server.

When you use `get` or `list_keys`, the output doesn't just appear in the chat history. LlamaIndex indexes this data, turning live API results into part of your searchable knowledge base for RAG applications. This means if you query past session configurations, the index can retrieve answers grounded in actual Redis key-value data, eliminating hallucinations.

Manage application state and metrics using the MCP Server.

You'll write RAG applications that track usage patterns. By calling `increment` or `set`, you store metrics like total logins or API calls counts. These updated numbers become part of the knowledge base, letting later queries reference the current operational status. The ability to use `delete` lets your index clean up old, irrelevant state data, keeping the vector store focused only on highly relevant and fresh information.

Confirm connection health in LlamaIndex.

The `ping` tool verifies that the Upstash Redis instance is reachable. Including this check ensures your RAG pipeline doesn't fail silently because of a network blip. Furthermore, using `get_key_info` lets you verify not only what data type a key holds, but also how much time is left before it expires, adding crucial context to the indexed knowledge.

Setup guide

Set up Upstash Redis MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Upstash Redis MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Upstash Redis tools.",
)
response = await agent.run("List recent Upstash Redis data")

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Common questions about Upstash Redis MCP in LlamaIndex

LlamaIndex ingests data from MCP Server tools like `get` and `list_keys`. Instead of just showing the raw output, it converts that key-value pair into embeddings, making the information searchable by meaning.

It acts as a live data source. When you run an application, it pulls current status—like counts via `increment` or configuration details via `get`—and feeds that real-time context into the vector index.

Absolutely. The `get_key_info` tool tells you the TTL (Time To Live) of a key. This helps LlamaIndex know if the data it's indexing is about to become stale, improving answer reliability.

Yes. You can use `increment` to count events or calls, and then index that running total. This lets you query things like 'How many times was this feature used last week?' directly from the knowledge base.

This MCP Server manages string and integer types. Because you control which keys are read via the tools, you maintain strict oversight over the type and scope of data indexed.

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