MCP Servers: Pre-warming?

[enxilium]

Currently, using just one MCP server causes a significant delay in session startup time. This makes it not ideal for a real-time AI assistant type of app. Are there any workarounds to minimizing the session startup time, for example pre-starting the MCP connections somehow?

[adk-bot]

Response from ADK Answering Agent (experimental, answer may be inaccurate)

TLDR: While there isn't a specific "pre-warming" feature for MCP connections in the ADK, you can significantly reduce session startup time by using remote MCP servers (via SSE or Streamable HTTP) instead of local stdio servers. The MCPSessionManager also includes session pooling to reuse existing connections, which helps minimize overhead.

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Hello! I see you're looking for ways to minimize MCP session startup time for your real-time AI assistant. That's a great question, as low latency is crucial for a good user experience.

Based on the ADK documentation, here are some recommendations and insights that might help:

1. Choose the Right Connection Type

The most significant factor in connection startup time is the type of MCP connection you're using. The ADK supports two main types:

• StdioConnectionParams: This is used for local MCP servers that run as a separate process on the same machine. While simple for development, starting a new process for each connection can introduce noticeable delays, which seems to be the issue you're encountering. [2]
• SseConnectionParams and StreamableHTTPConnectionParams: These are used for remote MCP servers that are accessed over the network. For production and high-volume applications, remote servers are the recommended approach. They can be scaled independently and avoid the overhead of process creation at connection time. [2]

For a real-time application, switching to a remote MCP server is likely the most effective way to reduce the startup delay.

2. Understand Session Management

The MCPSessionManager in the ADK is designed to handle MCP sessions efficiently. [9] It automatically manages the creation, caching, and reuse of sessions. When you request a tool from an MCPToolset, the MCPSessionManager will try to reuse an existing, connected session if one is available, rather than creating a new one from scratch. [9]

This session pooling mechanism is key to performance. By reusing sessions, you avoid the cost of re-establishing the connection and re-initializing the session for every request. This is particularly beneficial when using remote MCP servers, as it avoids repeated network handshakes.

3. No Explicit "Pre-warming" API

I couldn't find any specific API or configuration for "pre-warming" connections in the documentation. However, the combination of using a long-running, remote MCP server and relying on the MCPSessionManager's session pooling should provide a similar benefit. The first request will establish the connection, and subsequent requests will be much faster as they reuse the existing session.

Recommendations:

• For Production/Real-Time Apps: Deploy your MCP server as a separate, long-running service (e.g., on Cloud Run or GKE) and connect to it from your ADK agent using SseConnectionParams or StreamableHTTPConnectionParams. [2]
• Optimize Your MCP Server: Ensure that your MCP server itself is optimized for a fast startup and can handle concurrent connections efficiently.
• Leverage Session Pooling: The MCPSessionManager handles this automatically, so as long as you are using an MCPToolset, you are already benefiting from it. [9]

I hope this information helps you build a more responsive AI assistant! Let me know if you have any other questions.

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References:
[1] mcp-tools Documentation
[2] mcp_session_manager.py Source Code