MCP Talk
# MCP Talk
Inter-agent messaging via Model Context Protocol (MCP).
A lightweight messaging system that enables AI agents (Claude, Codex, Gemini, etc.) to communicate with each other in real-time through a shared message queue.
## Features
- **Simple tools**: `send`, `check`, `ack`, `broadcast`, `list`, `clean`, `reply`
- **File-based persistence**: Messages stored as JSON files for easy debugging
- **Namespace isolation**: Separate message queues per project
- **Cross-agent**: Works with any MCP-compatible AI assistant
- **Zero dependencies**: Just Python 3.10+ and the MCP SDK
## Installation
```bash
# From PyPI (recommended)
pipx install mcp-talk
# Or with pip
pip install mcp-talk
# From source
git clone https://github.com/devinvenable/mcp-talk.git
cd mcp-talk
pipx install .
```
## Configuration
Add to your MCP client configuration:
### Claude Desktop / Claude Code
```json
{
"mcpServers": {
"mcp-talk": {
"command": "mcp-talk"
}
}
}
```
### Using uvx (no install required)
```json
{
"mcpServers": {
"mcp-talk": {
"command": "uvx",
"args": ["mcp-talk"]
}
}
}
```
### Codex CLI (~/.codex/config.toml)
```toml
[mcp_servers.mcp-talk]
command = "mcp-talk"
```
### Gemini CLI (~/.gemini/settings.json)
```json
{
"mcpServers": {
"mcp-talk": {
"command": "mcp-talk"
}
}
}
```
## Tools
### `send` - Send a direct message
```
send(to="codex", message="Please review PR #123", from_agent="claude")
```
### `check` / `chk` - Check messages
```
check(agent="claude")
chk(agent="claude", include_body=true, auto_ack=true)
```
Returns up to 5 messages by default. Use `include_body=true` for full message text, `auto_ack=true` to delete after reading.
### `broadcast` - Send to all agents
```
broadcast(message="Standup in 5 minutes", from_agent="pm")
```
### `ack` - Acknowledge/delete a message
```
ack(id="20251126_143022_abc12345")
```
### `reply` - Reply to a message
```
reply(id="20251126_143022_abc12345", message="Done!", from_agent="gemini")
```
Automatically sends response to original sender and acknowledges the original message.
### `list` - List all messages (PM view)
```
list(limit=10, include_body=true)
```
### `clean` - Remove old messages
```
clean(hours=24)
```
## Namespaces
Isolate messages between projects using the `namespace` parameter:
```
# Game project
send(to="gemini", message="Review level 3", namespace="game")
check(agent="gemini", namespace="game")
# Work project
send(to="gemini", message="Review PR #123", namespace="work")
check(agent="gemini", namespace="work")
```
Messages are stored in separate directories:
```
~/.mcp_talk/q/ # default (no namespace)
~/.mcp_talk/q/game/ # namespace="game"
~/.mcp_talk/q/work/ # namespace="work"
```
## Message Format
Messages are stored as JSON files in `~/.mcp_talk/q/`:
```json
{
"id": "20251126_143022_abc12345",
"from": "claude",
"to": "codex",
"type": "direct",
"created": "2025-11-26T14:30:22+00:00",
"message": "Please review PR #123",
"namespace": "work"
}
```
## Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| `MCP_TALK_QUEUE` | `~/.mcp_talk/q/` | Message queue directory |
| `MCP_TALK_AUTO_CLEAN_HOURS` | `24` | Auto-delete messages older than N hours (0 to disable) |
| `MCP_TALK_MAX_MESSAGE_CHARS` | `2000` | Maximum message length |
## Multi-Agent Setup Tips
### Teaching agents to check messages
The `chk` shortcut is designed to be a simple keyword you can add to agent instructions. Add to your agent's system prompt or memory:
**Gemini** (`~/.gemini/instructions.md`):
```
When I read new messages, I should investigate the topic, verify assertions, and contribute my own expertise to the team.
```
**Claude** (CLAUDE.md in project):
```
When starting work, check for messages with: chk(agent="claude")
```
**Codex** (`~/.codex/instructions.md`):
```
Before starting tasks, check the message queue for any team communications.
```
### Recommended MCP config with env overrides
Customize behavior per-agent with environment variables:
```toml
# ~/.codex/config.toml
[mcp_servers.mcp-talk]
command = "mcp-talk"
env = { MCP_TALK_AUTO_CLEAN_HOURS = "12", MCP_TALK_MAX_MESSAGE_CHARS = "1200" }
```
```json
// ~/.gemini/settings.json
{
"mcpServers": {
"mcp-talk": {
"command": "mcp-talk",
"env": {
"MCP_TALK_AUTO_CLEAN_HOURS": "12",
"MCP_TALK_MAX_MESSAGE_CHARS": "1200"
}
}
}
}
```
## Example Workflow
1. **Claude** sends a task to Gemini:
```
send(to="gemini", message="Please review the authentication module", from_agent="claude")
```
2. **Gemini** checks for messages:
```
chk(agent="gemini")
```
3. **Gemini** replies when done:
```
reply(id="20251126_143022_abc12345", message="Review complete, LGTM!", from_agent="gemini")
```
4. **Claude** receives the reply:
```
chk(agent="claude")
```
## Development
```bash
# Clone and install in development mode
git clone https://github.com/devinvenable/mcp-talk.git
cd mcp-talk
pip install -e .
# Reinstall after changes (if using pipx)
pipx install --force .
```
## License
MIT
TDQS
Scored across 8 tools
Multiple tools have unclear boundaries and overlapping purposes. 'check' and 'chk' are explicitly redundant shortcuts for the same function, while 'ack', 'reply', and 'send' all involve message handling with potential confusion about when to use each. The descriptions help somewhat, but the overlap is significant.
Naming is inconsistent with mixed conventions. Most tools use short, imperative verbs (ack, broadcast, check, clean, list, reply, send), but 'chk' is an abbreviation that breaks this pattern. There's no consistent verb_noun structure, and the naming feels chaotic rather than following a predictable scheme.
With 8 tools, the count is reasonable for a messaging/queue management server. It's well-scoped for core operations like sending, receiving, acknowledging, and cleaning messages. The redundancy of 'check' and 'chk' slightly bloats it, but overall, the number of tools is appropriate for the domain.
The tool surface covers the essential messaging lifecycle well: sending (send, broadcast), receiving (check, chk, list), responding (reply), and cleanup (ack, clean). Minor gaps might include operations like message prioritization or filtering, but agents can likely work around these with the provided tools for basic workflows.