evc-team-relay-mcp

evc-team-relay-mcp

Read, search, and write Obsidian vault notes via Team Relay collaborative server. Supports shared folders, real-time sync, and AI agent access.

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README

EVC Team Relay — MCP Server

PyPI License: MIT MCP Entire VC

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.


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 with your Relay credentials.

<details> <summary><b>Claude Code</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_EMAIL": "agent@yourdomain.com",
        "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_EMAIL": "agent@yourdomain.com",
        "RELAY_PASSWORD": "your-password"
      }
    }
  }
}

</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_EMAIL": "agent@yourdomain.com",
        "RELAY_PASSWORD": "your-password"
      }
    }
  }
}

</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>

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

3. Use it

Your AI agent now has these tools:

Tool Description
authenticate Authenticate with credentials (auto-managed)
list_shares List accessible shares (filter by kind, ownership)
list_files List files in a folder share
read_file Read a file by path from a folder share
read_document Read document by doc_id (low-level)
upsert_file Create or update a file by path
write_document Write to a document by doc_id
delete_file Delete a file from a folder share

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
RELAY_CP_URL=https://cp.yourdomain.com \
RELAY_EMAIL=agent@yourdomain.com \
RELAY_PASSWORD=your-password \
docker compose up -d

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

Product What it does Link
Team Relay Self-hosted collaboration server repo
Team Relay Plugin Obsidian plugin for Team Relay repo
Relay MCP MCP server for AI agents this repo
OpenClaw Skill OpenClaw agent skill (bash) repo
Local Sync Vault <-> AI dev tools sync repo
Spark MCP MCP server for AI workflow catalog repo

Community

License

MIT

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