Onboard MCP Server

official

Connect your AI assistant to Onboard to monitor live onboarding projects, surface blocked tasks and risks, draft customer emails, and take action on tasks with role-based access and preview-first safety.

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Documentation

Platform

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Onboard MCP

MCP guides

MCP MCP for customer teams Introduction to MCP MCP setup Connecting to Onboard MCP MCP Registry Security & permissions (MCP)AI workflow examples MCP use cases — AI agents Example workflows Automation platforms & MCP

Desktop clients

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MCP

Model Context Protocol — connect AI assistants and desktop clients to Onboard.

Model Context Protocol (MCP) is an open standard for connecting AI assistants to tools and data. Onboard exposes MCP so your team can use operational AIfor onboarding copilots, launch coordination, and support workflows—not only generic chat.

What you get

Intent-based workflow tools — Search customers, list active projects, summarize onboarding status, load KPI dashboards, find blockers, draft updates, and preview writes—without hand-authoring REST calls.
Curated [API] tools — Full OpenAPI browse, explain, and run_api_request when you need raw routes.
Hosted MCP (HTTP) for clients that support remote MCP over HTTP, using long-lived sessions (for example Server-Sent Events) and JSON-RPC as documented on MCP setup.
OAuth or API key — Claude custom connectors can sign in per user; most desktop clients use an API key from Integrations → API.
Optional local stdio server for engineering workflows (for example running python -m onboard_mcp.server in a secure environment). Email [email protected] to confirm whether this is enabled for your tenant.

Security and access

MCP calls are only as safe as the credentials and network you use. Treat MCP like any privileged integration surface:

Use API keys and secrets only where your security policy allows, and rotate them on the same schedule as other production keys.
Prefer server-side or controlled desktop setups over pasting keys into untrusted tools.

For broader trust and compliance context, see Compliance & trust. For operational checks before go-live, see the Security checklist. For how API keys work with Onboard, see Authentication.

Learning path (curriculum)

MCP for customer teams — features, what teams do day to day, rollout (start here for business stakeholders)
Introduction to MCP — what MCP is and how Onboard uses it
MCP setup — production URLs, API key, authentication
Connecting to Onboard MCP — how to connect clients, OAuth vs API key, MCP Inspector, Client ID from Integrations
MCP Registry — official Model Context Protocol Registry listing (io.github.viaonboard/onboard)
Security & permissions (MCP) — keys, tenancy, reviews
AI workflow examples — daily brief, QBR prep, follow-up, launch week
MCP use cases — AI agents — Chief of Staff, set-up, onboarding, review, reporting, opportunity, and risk agents
Agent MCP playbook (engineers): in the API repo, docs/api/mcp-agent-tool-playbook.md and .cursor/skills/onboard-agent-* skills map each persona to workflow and [API] tools (including browse_inbox, list_projects, get_kpi_dashboard, and post_comment).
Example workflows — leadership review, launch coordination, meeting follow-up
Automation platforms & MCP — Zapier, Make, n8n, Workato vs MCP
MCP desktop clients — per-client setup guides (Claude, Quick, Cursor, …)

Setup highlights

MCP Registry — find Onboard on the official Model Context Protocol Registry (io.github.viaonboard/onboard).
MCP on Amazon Quick — field-by-field Quick configuration with a screenshot.
MCP on OpenAI Agent Builder — MCP tool node, + Server, and API key auth for workflows.

Related docs

Security checklist — keys, webhooks, MCP hygiene
AI assistant rules — copyable rules bundles for Cursor, Claude, Windsurf, and Paperclip when building against the REST API.
Onboard REST API — base URLs, auth, and API behavior that underpin MCP-backed tools.

How is this guide?

GoodBad

MCP for customer teamsWhat Onboard MCP is, what features it exposes, and how CS and onboarding teams use it day to day.

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