MCP for your agent

Connecting GeckoChat to Claude Code, Codex, OpenCode and Cursor

integrationsmcpagent

Updated September 22, 2026

MCP is the common way to hand an AI agent someone else's tools. Connect GeckoChat, and you talk to your agent in plain words — «show yesterday's orders», «put together a broadcast for everyone who didn't reach checkout» — while it calls the platform commands itself.

There is one address: https://api.geckochat.app/mcp, authorised by your key in the Authorization: Bearer … header.

Claude Code

bash
claude mcp add --transport http geckochat https://api.geckochat.app/mcp --header "Authorization: Bearer gk_YOUR_KEY"

Add -s user to make the connection work in every project, not only in the current folder.

Codex

bash
export GECKOCHAT_API_KEY=gk_YOUR_KEY
codex mcp add geckochat --url https://api.geckochat.app/mcp --bearer-token-env-var GECKOCHAT_API_KEY

Codex reads the key from an environment variable, so declare it where Codex starts — in ~/.zshrc, for instance.

OpenCode

An opencode.json file, in the project folder or in ~/.config/opencode/:

json
{
  "mcp": {
    "geckochat": {
      "type": "remote",
      "url": "https://api.geckochat.app/mcp",
      "headers": { "Authorization": "Bearer gk_YOUR_KEY" },
      "enabled": true
    }
  }
}

Cursor

The file ~/.cursor/mcp.json (or .cursor/mcp.json inside a project):

json
{
  "mcpServers": {
    "geckochat": {
      "url": "https://api.geckochat.app/mcp",
      "headers": { "Authorization": "Bearer gk_YOUR_KEY" }
    }
  }
}

Any other client

Anything that speaks remote MCP over HTTP will do: the address https://api.geckochat.app/mcp and the header Authorization: Bearer gk_YOUR_KEY. Nothing runs on your machine — the server is ours.

Checking it

Ask your agent to show your bots. It will call bot_list and answer with the list — that is the proof that the connection is live and the key was accepted.

MCP or the CLI

Both surfaces are the same commands; pick by the task.

MCPCLI
Who decides what to callthe agentyou
Good fora conversation: «work it out and do it»a precise question with a large answer
Where it livesinside your AI clientterminal, script, CI

An MCP answer lands in the conversation with the model in full, so a hundred-record list is expensive there. The same list in a terminal collapses into three lines: --count-by, --sum and --table are computed on the server. If your agent has both, tell it plainly: «count it with geckochat in the terminal».