Turn any AI tool into a peer-to-peer agent hub. Discover, communicate with, and orchestrate AI agents across any network -- encrypted, decentralized, zero config.
uvx agentanycast-mcpWorks with Claude Desktop, Claude Code, Cursor, VS Code, Windsurf, JetBrains, Gemini CLI, Amazon Q, Cline, Continue, Zed, Roo Code, and ChatGPT.
Pick your platform and add the config below. That's the entire setup -- the daemon downloads and starts automatically on first run.
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"agentanycast": {
"command": "uvx",
"args": ["agentanycast-mcp"]
}
}
}Claude Code
claude mcp add agentanycast -- uvx agentanycast-mcpCursor
Add to .cursor/mcp.json in your project root:
{
"mcpServers": {
"agentanycast": {
"command": "uvx",
"args": ["agentanycast-mcp"]
}
}
}VS Code + Copilot
Add to .vscode/mcp.json:
{
"servers": {
"agentanycast": {
"command": "uvx",
"args": ["agentanycast-mcp"]
}
}
}Windsurf
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"agentanycast": {
"command": "uvx",
"args": ["agentanycast-mcp"]
}
}
}JetBrains AI
Settings -> Tools -> AI -> MCP Servers -> Add:
{
"servers": {
"agentanycast": {
"command": "uvx",
"args": ["agentanycast-mcp"]
}
}
}Gemini CLI
Add to ~/.gemini/settings.json:
{
"mcpServers": {
"agentanycast": {
"command": "uvx",
"args": ["agentanycast-mcp"]
}
}
}Amazon Q Developer
Add to ~/.aws/amazonq/mcp.json:
{
"mcpServers": {
"agentanycast": {
"command": "uvx",
"args": ["agentanycast-mcp"]
}
}
}Cline
Add to Cline MCP settings (VS Code: Ctrl+Shift+P -> "Cline: MCP Servers"):
{
"mcpServers": {
"agentanycast": {
"command": "uvx",
"args": ["agentanycast-mcp"]
}
}
}Continue
Add to ~/.continue/config.json:
{
"experimental": {
"modelContextProtocolServers": [
{
"transport": {
"type": "stdio",
"command": "uvx",
"args": ["agentanycast-mcp"]
}
}
]
}
}Zed
Add to Zed settings (~/.config/zed/settings.json):
{
"context_servers": {
"agentanycast": {
"command": {
"path": "uvx",
"args": ["agentanycast-mcp"]
}
}
}
}Roo Code
Add to Roo Code MCP settings (VS Code: Ctrl+Shift+P -> "Roo Code: MCP Servers"):
{
"mcpServers": {
"agentanycast": {
"command": "uvx",
"args": ["agentanycast-mcp"]
}
}
}ChatGPT (HTTP mode)
Deploy the server with HTTP transport:
agentanycast-mcp --transport http --port 8080
# or: docker run -p 8080:8080 agentanycast/mcp-serverThen add http://your-server:8080/mcp in ChatGPT developer settings.
Once connected, ask your AI assistant:
- "Find agents that can translate Japanese" -- discovers agents on the P2P network
- "Send 'summarize this article' to the translate agent" -- encrypted task delivery
- "What agents are connected right now?" -- lists connected peers
- "What's my Peer ID?" -- shows your node's identity and DID
| Tool | Description | Example prompt |
|---|---|---|
discover_agents |
Find agents by skill | "Find agents that can translate" |
send_task |
Send an encrypted task to an agent | "Send 'hello' to peer 12D3KooW..." |
get_task_status |
Check the result of a sent task | "What was the result of that task?" |
get_agent_card |
Get an agent's capabilities | "What can that agent do?" |
list_connected_peers |
List all connected P2P peers | "Who's online?" |
get_node_info |
Get this node's Peer ID, DID, status | "What's my agent info?" |
Set these in the "env" section of your MCP config:
| Variable | Description | Default |
|---|---|---|
AGENTANYCAST_RELAY |
Relay server multiaddr for cross-network P2P | None (LAN only) |
AGENTANYCAST_HOME |
Data directory for daemon state | ~/.agentanycast |
Example with relay for cross-network communication:
{
"mcpServers": {
"agentanycast": {
"command": "uvx",
"args": ["agentanycast-mcp"],
"env": {
"AGENTANYCAST_RELAY": "/ip4/relay.agentanycast.io/tcp/4001/p2p/12D3KooW..."
}
}
}
}agentanycast-mcp [--transport stdio|http] [--port 8080] [--relay MULTIADDR] [--home DIR]
CLI arguments take priority over environment variables.
Your AI Tool (Claude, Cursor, VS Code, ...)
|
| MCP protocol (stdio or HTTP)
v
AgentAnycast MCP Server
|
| gRPC (Unix domain socket)
v
AgentAnycast Daemon (Go)
|
| libp2p (TCP/QUIC + Noise_XX encryption + NAT traversal)
v
Remote AI Agents (anywhere in the world)
- Zero config --
uvx agentanycast-mcphandles everything. The daemon is auto-downloaded and managed. - Zero API keys -- agents are identified by cryptographic Peer IDs (Ed25519), not accounts or tokens.
- End-to-end encrypted -- Noise_XX protocol. Even relay servers see only ciphertext.
- NAT traversal -- works behind firewalls with automatic hole-punching and relay fallback.
This is the only MCP server that connects to a decentralized peer-to-peer network of AI agents. Other MCP servers connect to specific SaaS APIs. AgentAnycast connects you to any AI agent, anywhere, with no intermediary that can read your messages.
Daemon fails to start
- Check that port 4001 (TCP) is not in use:
lsof -i :4001 - Try a clean state:
rm -rf ~/.agentanycast && uvx agentanycast-mcp
No agents found on discover
- Agents must be on the same LAN (mDNS) or connected to the same relay
- Set
AGENTANYCAST_RELAYto connect across networks
Connection timeout
- Behind a strict firewall? Set a relay address. The relay provides fallback connectivity.
- Check daemon logs:
cat ~/.agentanycast/daemon.log
"uvx" not found
- Install uv:
curl -LsSf https://astral.sh/uv/install.sh | sh - Or install directly:
pip install agentanycast-mcp
Tool calls failing
- Restart your AI tool after adding the MCP config
- Verify config JSON syntax (no trailing commas)
- AgentAnycast -- Main project, documentation, examples
- Python SDK -- Build P2P agents in Python
- TypeScript SDK -- Build P2P agents in TypeScript