Skip to main content

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

Project description

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

Sending is usually 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. Explicit send syntax exists—relay: <message> in Desktop, /relay <message> in Code—but you'll rarely need it.

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 ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "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 (or ~/.claude/.mcp.json for global):

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

3. Install the /relay 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() or /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 — mhcoen@alum.mit.edu · mhcoen@gmail.com

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mcp_server_relay-1.1.1.tar.gz (70.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mcp_server_relay-1.1.1-py3-none-any.whl (78.7 kB view details)

Uploaded Python 3

File details

Details for the file mcp_server_relay-1.1.1.tar.gz.

File metadata

  • Download URL: mcp_server_relay-1.1.1.tar.gz
  • Upload date:
  • Size: 70.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.2

File hashes

Hashes for mcp_server_relay-1.1.1.tar.gz
Algorithm Hash digest
SHA256 dc5317ae9506d663f0a86a0db559132d2927bb2a5166845fa21322f4f297df5e
MD5 75a2b68d58c910f58bde687c76a72a4d
BLAKE2b-256 38dcdd5744a26387ee65578b1053a0b8e52cf0e58e7845fe2509c410df952873

See more details on using hashes here.

File details

Details for the file mcp_server_relay-1.1.1-py3-none-any.whl.

File metadata

File hashes

Hashes for mcp_server_relay-1.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 b63d319d78dea13338e5e699624a6d39ac3c8f4f6ba7e08491a8dc231f588bd9
MD5 0f53d507b741aff674467bfbad66b2b0
BLAKE2b-256 3b08624ea8f26fb252e495e5cee6eb6bd1a40829c78cd552139e8df17d644083

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page