Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@gcontext-mcp list all files in my workspace"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
The local MCP connector for gcontext. Runs on your machine as a stdio MCP server, bridging your AI agent to your gcontext workspace in the cloud.
Structure (files, folders, types, secret names) lives in the cloud
Secret values and script execution stay local, on your machine
Add to Claude Code:
claude mcp add-json gcontext-personal '{"type":"stdio","command":"uvx","args":["gcontext-mcp"],"env":{"GCONTEXT_TOKEN":"<your token>"}}'Or run directly:
uvx gcontext-mcpRelated MCP server: colab-mcp
Variable | Required | Description |
| yes | Your workspace token (from app.gcontext.ai) |
| no | API endpoint (default: |
If you self-host the gcontext backend, point GCONTEXT_API_URL to your instance:
{"type":"stdio","command":"uvx","args":["gcontext-mcp"],"env":{"GCONTEXT_TOKEN":"<token>","GCONTEXT_API_URL":"http://localhost:5179"}}See self-hosting docs for backend setup.
The connector exposes MCP tools for reading, writing, searching, and managing files in your gcontext workspace. Secret values are stored locally (in ~/.gcontext/.env) and injected at script runtime without ever leaving your machine.
MIT
This server cannot be deployed
Securely search and manage workspace context files for AI agents and teams.
Git-backed platform for skills, tools, and context for AI agents
Shared project context for AI agents and teams: docs, tasks, and messages that stay current.
Shared, permission-aware company context for AI agents, with provenance, approvals and audit.
