mcp-server-relay 1.2.1

MCP relay server for Claude Desktop <-> Claude Code communication

Relay

A wonderfully simple tool that moves information—files, code, data, comments, directions, etc.—between Claude Desktop and Claude Code while keeping their main context windows separate.

Both Claudes can send messages autonomously—when they need input from the other side, they just do it. You get a system notification when something arrives. You fetch when you're ready.

Why? Desktop and Code have different strengths. Desktop is better for conversation—planning, brainstorming, reviewing, iterating on prose. Code is better for execution—editing files, running commands, working through errors. But they don't share context. If you draft something in Desktop and want Code to implement it, or you want Desktop's opinion on code you're writing, you're copy-pasting between apps.

Relay connects them without requiring you to manually specify questions and cut & paste between windows. This is basic functionality that should have been integrated by Anthropic.

You're always in control. Messages sit in the buffer until you explicitly fetch them. The models can't pull from the relay on their own—you have to type get or /get. This means there's no runaway inter-LLM conversation burning through your tokens. You see a notification, you decide whether to check it.

Quick Example

[In Desktop]
You:     Send my last error message to Code.
Desktop: [sends via relay]

[notification pops up in Code's terminal]

[In Code]
You:     /get (You can also type "get" but /get is faster.)
Code:    Got it. That's from line 47 in parser.py—the regex isn't matching.
         [fixes the bug]

Extended Example

[In Code]
You:     My classifier's stuck at 72% accuracy. Send the
         training config and last epoch's metrics to Desktop.
Code:    [sends config.yaml and metrics via relay]

[System notification appears that a message has been sent to Desktop]

[In Desktop]
You:     get
Desktop: [fetches config and metrics]
         The loss curve suggests you're underfitting. Learning
         rate 1e-5 is conservative for a model this size...

You:     What would you try?
Desktop: Bump to 3e-5, add warmup steps, and your batch size
         of 8 is small—if memory allows, try 16. Also the
         class imbalance is brutal: 80/15/5 split. Consider
         weighted loss or oversampling the minority classes.

You:     Specify the changes.
Desktop: [sends updated config via relay]

[System notification appears that a message has been sent to Code]

[In Code]
You:     /get
Code:    Got it. Updating config.yaml and adding class weights
         to the loss function.
         [edits files, starts training run]

         Epoch 12: accuracy up to 79%, but precision on
         class 2 dropped. Want Desktop's take?

You:     Yeah, send the confusion matrix.
Code:    [sends via relay]

[In Desktop]
You:     get
Desktop: Class 2 is getting confused with class 0—they may be
         semantically close. I need more examples.
         [automatically sends request to Code via relay]

Usage

Type get in Desktop or /get in Code to check for messages from the other side. That's the primary interaction.

Sending is easy and implicit. When you say "Ask Desktop if this looks right" or "Send the README to Code," the models recognize the intent and call the relay automatically. Models may also send messages on their own if they decide they need input from the other side.

Notifications

When a message arrives, you'll get a system notification so you know to check the other side. No need to poll manually.

Platform	Method	Notes
macOS	osascript	Native notification center
Linux	notify-send	Requires libnotify
Windows	PowerShell toast	Native toast notifications

Notifications include sound. Duration and display behavior are controlled by your OS settings.

Setup

What's uvx? uvx runs Python packages directly without installing them globally. It handles dependencies automatically. If you don't have it: curl -LsSf https://astral.sh/uv/install.sh | sh (See astral.sh/uv for more info.)

1. Configure Claude Desktop

Add to your Claude Desktop config:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "relay": {
      "command": "uvx",
      "args": ["mcp-server-relay", "--client", "desktop"]
    }
  }
}

Restart Claude Desktop.

2. Configure Claude Code

Add to .mcp.json in your project root:

{
  "mcpServers": {
    "relay": {
      "command": "uvx",
      "args": ["mcp-server-relay", "--client", "code"]
    }
  }
}

3. Install the /get slash command (optional)

uvx mcp-server-relay --setup-code

This copies the slash command to ~/.claude/commands/.

Design Notes

The relay is global. The buffer at ~/.relay_buffer.db is shared across all projects. Claude Desktop has no concept of which project you're working on—it's a general-purpose chat interface—so per-project isolation isn't practical. This is intentional: one user, one machine, one relay.

If you switch projects in Code, the relay comes with you. Old messages from the previous project may still be there; use relay_clear() if you want a fresh start. If you want separate conversations in Desktop for different projects, just start a new chat there.

Large files are slow. For messages a page or two in length, the relay is fast. For large files, it's faster to drag them directly into the interface you want. You can still send accompanying context via relay.

Tools

Tool	Description
relay_send(message, sender)	Send a message (sender: "desktop" or "code")
relay_fetch(limit, reader, unread_only)	Fetch recent messages, optionally mark as read
relay_clear()	Delete all messages from the buffer

Technical Details

• Buffer: SQLite at ~/.relay_buffer.db
• Rolling window: 20 messages max (oldest evicted first)
• Message limit: 64 KB per message
• Idle timeout: 1 hour (server exits automatically when inactive)
• Transport: stdio (standard MCP)
• Python: 3.9+

Author

Michael Coen — [e-mail] · [e-mail]