Where do I locate my Upstash REST URL and Token?

Sign into your Upstash management console and load your specific Redis cluster. Scroll down visually on the main 'Details' page until you hit the 'REST API' section block. You will see both your 'UPSTASH_REDIS_REST_URL' and the alphanumeric 'UPSTASH_REDIS_REST_TOKEN' ready to be copied.

Can the agent interact with standard Redis connections (redis://)?

No, this specific MCP connection module specifically utilizes Upstash's serverless HTTP/REST endpoints. It bypasses conventional persistent TCP Redis sockets to provide highly secure and connection-agnostic read/write integrations optimized for AI servers.

Are there limitations to wildcard patterns when doing list_keys?

Yes, standard Redis operational caveats apply. Searching broadly via a '*' (asterisk) scan across massively populated environments isn't recommended. Aim to request the AI narrow down patterns, like 'session:*' instead of blank wildcards.

Upstash Redis MCP Server for Pydantic AI 7 tools — connect in under 2 minutes

Name: Upstash Redis
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Upstash Redis through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Upstash Redis "
            "(7 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Upstash Redis?"
    )
    print(result.data)

asyncio.run(main())

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* 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.

Pydantic AI validates every Upstash Redis tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Upstash Redis MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Upstash Redis MCP Server

Pydantic AI provides unique advantages when paired with Upstash Redis through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Upstash Redis integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Upstash Redis connection logic from agent behavior for testable, maintainable code

Upstash Redis + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Upstash Redis MCP Server delivers measurable value.

Type-safe data pipelines: query Upstash Redis with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Upstash Redis tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Upstash Redis and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Upstash Redis responses and write comprehensive agent tests

Upstash Redis MCP Tools for Pydantic AI (7)

These 7 tools become available when you connect Upstash Redis to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Upstash Redis to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Upstash Redis + Pydantic AI FAQ

Common questions about integrating Upstash Redis MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Upstash Redis MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.