EVC Team Relay - MCP Server

Give your AI agent read/write access to your Obsidian vault.

Your agent reads your notes, creates new ones, and stays in sync — all through the Team Relay API.

Works with Claude Code, Codex CLI, OpenCode, and any MCP-compatible client.

<a href="https://glama.ai/mcp/servers/@entire-vc/evc-team-relay-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@entire-vc/evc-team-relay-mcp/badge" alt="evc-team-relay-mcp MCP server" /> </a>

Quick Start

1. Install

Option A — from PyPI (recommended):

No installation needed — uvx downloads and runs automatically. Skip to step 2.

Option B — from source:

git clone https://github.com/entire-vc/evc-team-relay-mcp.git
cd evc-team-relay-mcp
uv sync   # or: pip install .

2. Configure your AI tool

Add the MCP server to your tool's config. Choose one authentication method:

Agent key (recommended) — create a key in the Obsidian plugin → Team Relay settings → Agent Keys. Supports read and write: list_files, read_file, tr_search, and upsert_file all work with a single key. Quickstart →

Email + password — use a dedicated agent account on your Relay instance.

<details> <summary><b>Claude Code — agent key</b></summary>

Add to .mcp.json in your project root or ~/.claude/.mcp.json:

{
  "mcpServers": {
    "evc-relay": {
      "command": "uvx",
      "args": ["evc-team-relay-mcp"],
      "env": {
        "RELAY_CP_URL": "https://cp.yourdomain.com",
        "RELAY_AGENT_KEY": "tr_agent_your_key_here"
      }
    }
  }
}

</details>

<details> <summary><b>Claude Code — email/password</b></summary>

{
  "mcpServers": {
    "evc-relay": {
      "command": "uvx",
      "args": ["evc-team-relay-mcp"],
      "env": {
        "RELAY_CP_URL": "https://cp.yourdomain.com",
        "RELAY_EMAIL": "[email protected]",
        "RELAY_PASSWORD": "your-password"
      }
    }
  }
}

</details>

<details> <summary><b>Codex CLI</b></summary>

Add to your codex.json:

{
  "mcp_servers": {
    "evc-relay": {
      "type": "stdio",
      "command": "uvx",
      "args": ["evc-team-relay-mcp"],
      "env": {
        "RELAY_CP_URL": "https://cp.yourdomain.com",
        "RELAY_AGENT_KEY": "tr_agent_your_key_here"
      }
    }
  }
}

</details>

<details> <summary><b>OpenCode</b></summary>

Add to opencode.json:

{
  "mcpServers": {
    "evc-relay": {
      "command": "uvx",
      "args": ["evc-team-relay-mcp"],
      "env": {
        "RELAY_CP_URL": "https://cp.yourdomain.com",
        "RELAY_AGENT_KEY": "tr_agent_your_key_here"
      }
    }
  }
}

</details>

<details> <summary><b>From source (all tools)</b></summary>

If you installed from source instead of PyPI, replace "command": "uvx" / "args": ["evc-team-relay-mcp"] with:

"command": "uv",
"args": ["run", "--directory", "/path/to/evc-team-relay-mcp", "relay_mcp.py"]

</details>

Environment variables:

Ready-to-copy config templates are also in config/.

3. Use it

Your AI agent now has these tools:

Typical workflow: list_shares -> list_files -> read_file / upsert_file

Authentication is automatic — the server logs in and refreshes tokens internally.

Remote Deployment (HTTP Transport)

For shared or server-side deployments, run as an HTTP server:

# Direct
uv run relay_mcp.py --transport http --port 8888

# Docker (pulls from Docker Hub automatically)
RELAY_CP_URL=https://cp.yourdomain.com \
[email protected] \
RELAY_PASSWORD=your-password \
docker compose up -d

# Or pull explicitly
docker pull deadalusevc/evc-team-relay-mcp:latest

Then configure your MCP client to connect via HTTP:

{
  "mcpServers": {
    "evc-relay": {
      "type": "streamable-http",
      "url": "http://your-server:8888/mcp"
    }
  }
}

Security

The MCP server provides significant security advantages over shell-based integrations:

• No shell execution — all operations are Python function calls via JSON-RPC, eliminating command injection risks
• No CLI arguments — credentials and tokens are never passed as process arguments (invisible in ps output)
• Automatic token management — the server handles login, JWT refresh, and token lifecycle internally; the agent never touches raw tokens
• Typed inputs — all parameters are validated against JSON Schema before execution
• Single persistent process — no per-call shell spawning, no environment leakage between invocations

Note: If you're using the OpenClaw skill (bash scripts), consider migrating to this MCP server for a more secure and maintainable integration.

How It Works

┌─────────────┐      MCP        ┌──────────────┐     REST API     ┌──────────────┐     Yjs CRDT      ┌──────────────┐
│  AI Agent   │ ◄────────────► │  MCP Server  │ ◄─────────────► │  Team Relay  │ ◄──────────────► │   Obsidian   │
│ (any tool)  │  stdio / HTTP  │ (this repo)  │    read/write   │   Server     │    real-time     │    Client    │
└─────────────┘                └──────────────┘                 └──────────────┘      sync         └──────────────┘

The MCP server wraps Team Relay's REST API into standard MCP tools. Team Relay stores documents as Yjs CRDTs and syncs them to Obsidian clients in real-time. Changes made by the agent appear in Obsidian instantly — and vice versa.

Prerequisites

• Python 3.10+ with uv (recommended) or pip
• A running EVC Team Relay instance (self-hosted or hosted)
• A user account on the Relay control plane

Part of the Entire VC Toolbox

Community

• entire.vc
• Discussions
• [email protected]

[//]: # (mcp-name: io.github.entire-vc/evc-team-relay-mcp)

License

MIT