Codex Setup
Configure Berry as an MCP server in OpenAI Codex CLI
Prerequisites
- Berry installed (
pip install berryorpipx install berry) - Berry authenticated (
berry auth login; orberry auth setfor CI/headless)
Option 1: Run berry init
The simplest way. From your project root:
berry initThis creates .codex/config.toml with the Berry server configuration.
Use Berry skills in Codex
In Codex, you can invoke a skill by typing $ and the skill name (or use /skills to browse).
Search & Learn (verified)
Workflow: Search & Learn
$search_and_learn_verified
How does <feature> work in this repo? Cite evidence spans and verify.RCA Fix Agent
Workflow: Refactoring & Bug Fixes
$rca_fix_agent
Bug: <describe failure + repro steps>. Provide root cause, fix, and tests.Option 2: Manual configuration
Create .codex/config.toml in your project root:
[mcp_servers.berry]
command = "berry"
args = ["mcp", "--server", "classic"]
[mcp_servers.berry.env]
OPENAI_API_KEY = "sk-..."
BERRY_SERVICE_URL = "http://..."Note: Codex uses TOML format, not JSON. The structure uses [mcp_servers.name]sections for each MCP server.
Tip: if you run berry auth login first, berry init will embed env defaults from ~/.berry/mcp_env.json for you.
Verify it works
- Start Codex in your project directory
- Check that Berry tools are available
- Try calling
list_spansto confirm the connection
Config file location
| File | .codex/config.toml |
| Format | TOML |
| Scope | Per-project (in project root) |