How to Use the Upstash Redis MCP in Pydantic AI

Build correct agents: Use Upstash Redis with Pydantic AI for validated data operations.

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Connect Upstash Redis MCP to Pydantic AI

Create your Vinkius account to connect Upstash Redis to Pydantic AI 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 Pydantic AI

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Setting and Expiration in Pydantic AI

The `set` tool lets your agent write a string value while also setting an expiry time. This is critical because it ensures that the data your agent uses won't become stale or misleading over time.

Fetching and Validating Data via MCP Server

To read data, use `get`. Because you’re running Pydantic AI, any retrieved value must conform to a defined type structure. The agent fails loudly if the data is wrong—no silent corruption here.

Tracking Changes with Pydantic AI

Need to track usage or state changes? Use `increment` and `decrement`. These tools let your agent maintain accurate numeric counts, which you can then validate against a schema.

Setup guide

Set up Upstash Redis MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "upstash-redis-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Upstash Redis tools.",
)

result = await agent.run("List recent Upstash Redis transactions")
print(result.output)

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Common questions about Upstash Redis MCP in Pydantic AI

It provides the raw key/value state that your agent must then process. The real guardrail comes from Pydantic AI, which validates the data coming *out* of this MCP Server.

It handles simple string values. If your 'complex' record is just a serialized JSON string, it works fine. But remember, the validation step happens in Pydantic AI.

It stores simple key-value strings. This means your agent must assume that whatever it reads or writes is a basic text blob, even if the schema defines it as something else.

It provides reliable state management. However, Pydantic AI's strength is in validation *after* retrieval. If the key was deleted via `delete`, no tool can bring it back.

This server touches simple key-value string data. Always audit who has access to the endpoint token and ensure that the agent only reads keys it absolutely needs.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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