Upstash MCP Server Integration

What does “Upstash” MCP Server do?

The Upstash MCP (Model Context Protocol) Server acts as a bridge between AI assistants and the Upstash Developer API. By implementing the standardized MCP protocol, it enables AI clients to perform a range of cloud database management tasks via natural language or programmatic commands. Through this server, LLMs and other AI tools can create or list Redis databases, manage keys, trigger backups, and analyze metrics such as throughput—all without requiring manual navigation of cloud dashboards. This integration streamlines developer workflows and empowers automated or conversational agents to interact directly with Upstash’s serverless data services, enhancing productivity and enabling sophisticated automation in cloud resource management.

List of Prompts

No prompt templates mentioned in the provided content.

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List of Resources

No explicit resources are detailed in the provided content.

List of Tools

No direct listing of tools found in the provided content or server.py. However, based on the usage examples, the server likely enables actions such as:

• Create a new Redis database
• List databases
• List keys with a pattern in a given database
• Create a backup
• Retrieve throughput analytics

But without direct code or documentation, these cannot be confirmed as discrete “tools” in the MCP sense.

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Use Cases of this MCP Server

• Cloud Database Provisioning: Developers can use AI assistants to create new Redis database instances in specific regions (e.g., us-east-1) using natural language commands, reducing manual setup time.
• Database Inventory Management: List all Upstash databases tied to an account, making it easier to audit, monitor, or manage resources programmatically or via conversational agents.
• Key Management and Querying: Retrieve keys matching specific patterns (e.g., “user:” in users-db) for rapid data inspection or cleanup, enhancing operational agility.
• Backup Automation: Trigger database backups via MCP-enabled automation, ensuring data resilience and compliance with minimal manual intervention.
• Performance Analytics: Request metrics such as spikes in throughput for the last 7 days, aiding in performance monitoring and troubleshooting.

How to set it up

Windsurf

1. Prerequisites: Ensure Node.js >= v18.0.0 is installed and obtain your Upstash API key and email.
2. Automatic Install:
   Run:
   npx -y @smithery/cli@latest install @upstash/mcp-server --client windsurf
3. Manual Config:
   Add to your Windsurf MCP config:

   npx -y @upstash/mcp-server run <UPSTASH_EMAIL> <UPSTASH_API_KEY>
   

4. Save and Restart: Apply changes and restart the client.
5. Verify: Test by triggering an MCP command in Windsurf.

Example JSON:

{
  "mcpServers": {
    "upstash": {
      "command": "npx",
      "args": ["-y", "@upstash/mcp-server", "run", "<UPSTASH_EMAIL>", "<UPSTASH_API_KEY>"]
    }
  }
}

Claude

1. Prerequisites: Install Node.js >= v18.0.0 and obtain Upstash API credentials.
2. Automatic Install:
   Run:
   npx -y @smithery/cli@latest install @upstash/mcp-server --client claude
3. Manual Config:
   npx @upstash/mcp-server init <UPSTASH_EMAIL> <UPSTASH_API_KEY>
4. Edit MCP Config: Confirm that Upstash MCP is added to your config file.
5. Verify: Use Claude Desktop to issue Upstash-related MCP commands.

Example JSON:

{
  "mcpServers": {
    "upstash": {
      "command": "npx",
      "args": ["@upstash/mcp-server", "init", "<UPSTASH_EMAIL>", "<UPSTASH_API_KEY>"]
    }
  }
}

Cursor

1. Prerequisites: Node.js >= v18.0.0, API key and email.
2. Automatic Install:
   Run:
   npx -y @smithery/cli@latest install @upstash/mcp-server --client cursor
3. Manual Config:
   Add to Cursor MCP config:
   npx -y @upstash/mcp-server run <UPSTASH_EMAIL> <UPSTASH_API_KEY>
4. Save/Restart: Apply and restart Cursor.
5. Verification: Issue Upstash MCP requests.

Example JSON:

{
  "mcpServers": {
    "upstash": {
      "command": "npx",
      "args": ["-y", "@upstash/mcp-server", "run", "<UPSTASH_EMAIL>", "<UPSTASH_API_KEY>"]
    }
  }
}

Cline

No specific instructions found for Cline in the provided content.

Securing API Keys

To secure API keys, use environment variables. Example:

{
  "mcpServers": {
    "upstash": {
      "command": "npx",
      "args": ["-y", "@upstash/mcp-server", "run"],
      "env": {
        "UPSTASH_EMAIL": "<UPSTASH_EMAIL>",
        "UPSTASH_API_KEY": "<UPSTASH_API_KEY>"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "upstash": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “upstash” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the tables above, the Upstash MCP Server provides solid setup instructions and a clear conceptual overview, but lacks detail on MCP primitives (prompts, resources, tools, roots, sampling) in its documentation. This limits its immediate usability for more advanced MCP integrations.

Our opinion

MCP Score: 5/10.
The Upstash MCP Server is easy to set up and well described in terms of its goal and supported platforms. However, it’s missing explicit documentation on prompts, resources, exposed tools, and advanced MCP features (roots, sampling), which are critical for developers seeking deep integration.

MCP Score