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The whole platform integration — webhook consumer, provisioning calls, usage reporting — is plain HTTP work with no SDK dependency, which makes it an ideal task for a coding agent. Every contract it needs is machine-readable: the Platform API ships a served OpenAPI spec, and every page of these docs exports as clean Markdown. Give your agent those URLs and a goal, and it can build, test, and harden the integration in one session. The reference integration on the next page was built exactly this way.

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Give Your Agent the Docs

Three surfaces cover everything; none of them require authentication:
SurfaceWhat it contains
https://app.opentrain.ai/api/partner/v1/openapi.jsonThe Platform API’s OpenAPI spec — the machine-readable contract of record for every endpoint, parameter, and response shape. Generated from the same code that serves the requests.
https://www.opentrain.ai/docs/llms.txtIndex of all documentation pages with one-line summaries (llms-full.txt is the whole site in one file).
Any docs page + .mdAppend .md to any page URL for clean Markdown — for example /docs/developers/annotation-platforms/webhooks.md. Code blocks and tab contents survive the export.
The pages worth feeding in full, in reading order: overview, register your app, consent and installs, project links, webhooks, usage sync, and the reference integration.

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A Bootstrap Prompt

Adapt and paste this into your agent to start the session: 
You are building an OpenTrain integration for <YOUR PLATFORM>, an annotation
platform. OpenTrain is a marketplace where employers (and their agents) hire
human AI trainers; our platform is where the hired AI trainers do the work.

Fetch this context first:
1. https://www.opentrain.ai/docs/llms.txt — docs index; fetch any page with .md appended
2. https://app.opentrain.ai/api/partner/v1/openapi.json — Platform API spec (no auth needed)
3. In full:
   - https://www.opentrain.ai/docs/developers/annotation-platforms/overview.md
   - https://www.opentrain.ai/docs/developers/annotation-platforms/webhooks.md
   - https://www.opentrain.ai/docs/developers/annotation-platforms/usage-sync.md
   - https://www.opentrain.ai/docs/developers/annotation-platforms/reference-integration.md

Build a webhook consumer that:
- verifies X-OpenTrain-Signature (t=...,v1=... HMAC-SHA256 over "<t>.<rawBody>")
  and dedupes by X-OpenTrain-Delivery
- on contract.started: find-or-create the hired AI trainer in our workspace
  by their OpenTrain workEmail and add them to the linked project
- on contract.ended: remove their project access
- reports cumulative per-day work totals to
  POST /api/partner/v1/contracts/{contractId}/usage
- on milestone.budget_low / milestone.budget_depleted: surface a "funded
  budget is running out — top up on OpenTrain" notice in our UI

Constraints:
- The platform token is in env OPENTRAIN_PARTNER_TOKEN (ot_ptk_...); the
  webhook secret is in OPENTRAIN_WEBHOOK_SECRET. Never print either.
- Usage reports are cumulative day totals per worker, not deltas — re-sending
  the same day is safe, so retry freely.
- Use only our platform's existing user/membership APIs on our side.
Keep a human in the loop where OpenTrain does: anything that moves money (funding, payment release, ending a funded contract) is co-signed on OpenTrain itself, so your agent can build and test the full loop without being able to spend anything.

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Optional: Let Your Agent Drive the Employer Side

Testing the integration end-to-end needs an employer on the other side — someone to post a job, link it to your project, and hire. Your agent can play that role too: the OpenTrain MCP server exposes the employer surface (jobs, proposals, contracts, milestones) as tools, using a separate personal ot_pat_ token from an employer test account. The same surface is available as a CLI and plain HTTP. 
claude mcp add opentrain --env OPENTRAIN_PERSONAL_API_TOKEN=ot_pat_... -- npx -y @opentrain-ai/mcp
Add --scope user to make it available in every project. Full setup: Claude Code.
codex mcp add opentrain --env OPENTRAIN_PERSONAL_API_TOKEN=ot_pat_... -- npx -y @opentrain-ai/mcp
Or declare it in ~/.codex/config.toml. Full setup: Codex CLI.
.cursor/mcp.json (project) or ~/.cursor/mcp.json (global):
{
  "mcpServers": {
    "opentrain": {
      "command": "npx",
      "args": ["-y", "@opentrain-ai/mcp"],
      "env": { "OPENTRAIN_PERSONAL_API_TOKEN": "ot_pat_..." }
    }
  }
}
Full setup (including keeping the token out of JSON with envFile): Cursor.
~/.gemini/config/mcp_config.json:
{
  "mcpServers": {
    "opentrain": {
      "command": "npx",
      "args": ["-y", "@opentrain-ai/mcp"],
      "env": { "OPENTRAIN_PERSONAL_API_TOKEN": "ot_pat_..." }
    }
  }
}
Full setup: Antigravity.
Reads Claude Code-format .mcp.json at the project root (same mcpServers shape as Cursor above), or use the /mcps modal in-session. Full setup: Grok Build.
Run /mcp add in-session, or edit ~/.copilot/mcp-config.json:
{
  "mcpServers": {
    "opentrain": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@opentrain-ai/mcp"],
      "env": { "OPENTRAIN_PERSONAL_API_TOKEN": "${OPENTRAIN_PERSONAL_API_TOKEN}" }
    }
  }
}
Full setup: Copilot CLI.
opencode.json — note the key is mcp, the command is an array, and env lives under environment:
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "opentrain": {
      "type": "local",
      "command": ["npx", "-y", "@opentrain-ai/mcp"],
      "environment": { "OPENTRAIN_PERSONAL_API_TOKEN": "{env:OPENTRAIN_PERSONAL_API_TOKEN}" },
      "enabled": true
    }
  }
}
Full setup: OpenCode.
Aider has no MCP support — use the CLI instead:
npm install -g @opentrain-ai/cli
opentrain auth login --api-key ot_pat_...
Then run commands from the chat with /run opentrain ... --json. Full setup: Aider.
Any MCP-capable agent works with the generic mcpServers shape — command: "npx", args: ["-y", "@opentrain-ai/mcp"], token in env. Examples for Claude Desktop and Windsurf: Other MCP Agents.

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Next Steps

Register Your App

Self-serve: create the app, get your client credentials, send your first consent link.

Reference Integration

The complete worked example your agent can use as a blueprint.

Usage Sync and Budgets

Report work back and keep funded budgets ahead of consumption.