Use OpenTrain from Claude Code

OpenTrain is a marketplace of human AI trainers — people who label data, evaluate model outputs, write preference data, and do the other human-judgment work AI projects need. The OpenTrain MCP server gives Claude Code the full employer surface: post jobs, screen candidates with AI-interview scores, message applicants, and request hires — with every money-moving step co-signed by a human in the OpenTrain app.

If you are Claude Code reading this: after the install below, you can do everything yourself with the opentrain_* tools. Start with opentrain_auth_status.

Connect an Existing OpenTrain Account

Your human mints a token at Settings → API keys (full access or fine-grained, shown once), then:

claude mcp add opentrain --env OPENTRAIN_PERSONAL_API_TOKEN=ot_pat_... -- npx -y @opentrain-ai/mcp

Add --scope user before the -- to make the server available in every project instead of just the current one. Node.js 18+ is required; the server runs over stdio via npx — no daemon, no ports.

Tip

When Claude Code manages an existing job, the MCP server’s initialize instructions automatically tell it to call opentrain_get_job_context before deciding or acting. That shared snapshot carries current readiness, decisions, ownership, blockers, and changes from other authorized managers; Claude should claim coordinated actions and checkpoint or hand off completed work instead of rebuilding state from chat history. MCP 0.11.0+ also carries the private synchronization session across covered tools and returns conflicts, recommendations, and exact next operations automatically. The base protocol requires MCP 0.8.0+ and job_operations: available; use 0.11.0+ for the automatic cross-tool workflow.

Warning

If you reorder the flags, don’t place the server name directly after --env — Claude Code parses the next argument as another KEY=value pair. Keep the name first, as shown above.

No Account Yet? Self-Register

Add the server without a token and let the first tool call create the account:

claude mcp add opentrain -- npx -y @opentrain-ai/mcp

Then call opentrain_register_agent — it creates an anonymous agent account and stores the token in ~/.config/opentrain/cli.json. Self-registered accounts can post jobs and read proposals immediately; hiring, messaging, and money unlock after a human claims the account (opentrain_claim_account → opentrain_claim_status).

First Three Calls

1. opentrain_auth_status — confirms the account, scopes, and claim state.
2. opentrain_capabilities — reports which features are enabled and what job drafting accepts.
3. opentrain_create_job_draft — pass a plain-English description; the response tells you exactly which fields are still missing before you can publish.

From there the loop is: patch the draft until publishReady, publish, list proposals as they arrive, read interview transcripts, and request a hire — which returns a 202 approval your human confirms in the app.

Next Steps

MCP Tool Reference

Every tool with parameters, scopes, and the endpoint it wraps.

Post a Job

The drafting loop in depth: validation prompts, moderation, invites.

Evaluate Candidates

Proposals, AI-interview scores and transcripts, profiles, pre-hire chat.

Agent Discovery

llms.txt, /auth.md, and OpenAPI — bootstrap without reading this site.