Connect Rafter to Codex
Codex speaks stdio, Rafter is a hosted server — mcp-remote bridges the two. One config block and Codex can search your team's shared memory and save learnings back.
Add Rafter to config.toml
Add this to
~/.codex/config.toml.[mcp_servers.rafter] command = "npx" args = ["-y", "mcp-remote", "https://app.heyrafter.xyz/api/mcp"]
npx needs Node 18+ on your PATH.
Restart Codex and sign in
Restart
codex. On the first Rafter call the sign-in link is printed — open it, authenticate, and pick the workspace to connect.Ask something your team already knows
Try “Search Rafter for our release checklist and follow it.”
Your first five minutes
The connection is the easy part. The habit is what pays: make Codex check the shared brain before it improvises. Three things to try in Codex:
- “What do we know about [a client or project]?”Rafter’s search answers from your team’s actual notes — with what they cite.
- “How do we [something a teammate has done before]? Follow the skill if we have one.”Skills are the team’s way of doing it, taught once and reused.
- “Save this as a memory: [a decision you just made].”Written back with add_memory — versioned, searchable, there for the next person.
To make it automatic, paste this line into Codex’s project or system instructions:
Check Rafter first: search our skills and memories before you answer, and save anything durable back with add_memory.
A longer startup prompt and a ready-made Claude Code skill live in the rafter-mcp repo.
Troubleshooting
Why the mcp-remote bridge?
Codex talks to MCP servers over stdio; Rafter is a hosted HTTP server. mcp-remote is the standard bridge between the two and handles the OAuth flow.
Which workspace should I pick when signing in?
The one whose skills and memories you want this tool to see. A connection sees exactly what you can see — your teams and your role — never more. You can connect again later and grant a different workspace.
Is my team's content used to train AI models?
No. Rafter does not use your content to train generalized or foundation models — see the privacy policy.
Give your whole team the same brain.From $9.99 per user — see pricing.
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