Onboard.io Implementation MCP. Track customer lifecycle data from any prompt.
Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Onboard.io Implementation connects your AI agent directly to Onboard.io. It lets you check customer onboarding plans, track specific tasks and milestones, view team assignments, and pull progress analytics—all without opening the portal.
Manage entire client lifecycles from a single conversation.
What your AI agents can do
Get member details
Gets the full profile information for a specific team member by ID.
Get onboarding customer details
Pulls all core data and current stage details for a client account.
Get plan details
Retrieves comprehensive metadata for one specific implementation plan ID.
Retrieves a list of every current customer onboarding plan, providing high-level status summaries.
Calculates and returns health scores or percentage completion for any specific implementation plan.
Retrieves details on a single task, including its owner, current status, and required deadline.
Fetches the core profile information for any client currently in the onboarding pipeline.
Lists all internal staff and specialists assigned to manage an onboarding plan.
Pulls a chronological list of all internal comments or communication notes attached to a specific plan ID.
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Onboard.io Implementation MCP Server: 10 Tools for Project Management
Use these tools to query every aspect of your client onboarding process—from listing all active plans to checking individual task assignments.
019d75e5get member details
Gets the full profile information for a specific team member by ID.
019d75e5get onboarding customer details
Pulls all core data and current stage details for a client account.
019d75e5get plan details
Retrieves comprehensive metadata for one specific implementation plan ID.
019d75e5get plan progress analytics
Calculates and returns health scores and percentage completion metrics for a given plan.
019d75e5get task details
Provides full status, assignment, and due date information for a single task ID.
019d75e5list onboarding customers
Lists all customers in the onboarding pipeline, supporting pagination via limit/offset.
019d75e5list onboarding plans
Retrieves a list of all implementation plans, allowing filtering by status (active/archived).
019d75e5list plan comments
Fetches the historical discussion and internal comments tied to an onboarding plan ID.
019d75e5list plan tasks
Lists all tasks associated with a specific plan, showing dependencies between them.
019d75e5list team members
Retrieves the full directory of internal team members assigned to onboarding projects.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
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- Built in DLP, auth, and compliance on every call
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- Publish to catalog or keep private
Make Your AI Do More
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- Use this MCP plus 4,700+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
You connect your AI agent right into Onboard.io, so you don't have to jump between dashboards just to get data. This server lets your agent manage client lifecycle workflows using natural language commands, keeping everything in one conversation. You’ll use it when you need to check customer plans, track specific milestones, see who owns what tasks, and pull progress analytics—all without ever opening the actual portal.
To start, your agent can get a list of all active implementation plans using list_onboarding_plans, which lets you filter for both active or archived statuses. Once you've got the plan ID, you can grab the full metadata using get_plan_details.
For tracking client status, your agent pulls core data and current stage details for a specific client account via get_onboarding_customer_details, letting you monitor their profile at any point in the pipeline. You've got list_onboarding_customers too; it lists every customer currently in the onboarding queue, and you can even paginate through large groups of accounts by specifying limits or offsets.
When you need to know how healthy a plan is, your agent runs metrics using get_plan_progress_analytics. This returns the health score and percentage completion for any given implementation plan. You'll also find that you can check out all the internal history attached to a specific plan ID by running list_plan_comments, which pulls every discussion note or communication thread.
Dealing with tasks is straightforward. Your agent lists all associated steps using list_plan_tasks, and what’s crucial here is it shows the dependencies between those tasks. For any single task, you get full status, assignment, and due date info by calling get_task_details using a specific task ID.
On the people side of things, your agent can pull the whole directory of internal staff assigned to onboarding projects with list_team_members. If you need details on one person, it uses get_member_details by pulling their full profile based on their ID. You'll also always be able to see which specialists are assigned to a plan by listing all team members connected to that project.
Basically, your agent can check the whole deal: you get the client’s current status with get_onboarding_customer_details, then you list every active plan using list_onboarding_plans. You'll grab the health score for a specific plan with get_plan_progress_analytics. If you need to dive into tasks, your agent first lists them all via list_plan_tasks and then gets deep details on any single task ID.
Need to know who’s working on it or what they missed? You check the assigned team members using list_team_members, pulling individual profiles with get_member_details. If you need to review history, your agent pulls all internal notes via list_plan_comments.
How Onboard.io Implementation MCP Works
- 1 Subscribe to the Onboard.io server and enter your API Bearer Token.
- 2 Tell your AI agent what you need (e.g., 'What's the status of the Enterprise Launch plan?').
- 3 The agent calls the correct tool, pulls the data from Onboard.io, and presents a plain-text summary.
The bottom line is your AI client acts as a unified front-end, accessing project data that was previously siloed across multiple internal tools.
Who Is Onboard.io Implementation MCP For?
Implementation Managers and Customer Success Leads who are tired of clicking through five different dashboards just to get a status update. If your job involves regularly asking 'Where are we on X?', this saves hours of context switching.
Uses the agent to monitor onboarding health across multiple clients, checking things like progress via get_plan_progress_analytics and viewing assigned team members using list_team_members.
Checks task statuses quickly. Needs to know if a critical milestone is overdue or who needs to be looped into the plan comments (list_plan_comments).
Automates data retrieval for weekly status reports, gathering customer profile info using get_onboarding_customer_details and listing all active plans with list_onboarding_plans.
What Changes When You Connect
- Check current plan status instantly. Instead of clicking through the Onboard.io dashboard, you can ask for a progress summary using list_onboarding_plans and get immediate results.
- Know who's responsible for what. Use list_plan_tasks or get_task_details to verify task ownership and deadlines without digging into multiple project boards.
- Audit client history fast. Pull all internal discussion points—comments, decisions, changes—using list_plan_comments, giving you full context on why a plan is stalled.
- See the big picture health score. Skip manual calculations and use get_plan_progress_analytics to pull high-level metrics for leadership reports instantly.
- Get customer details without logging in. Use get_onboarding_customer_details to verify client status right from your chat window, keeping your workflow focused.
Real-World Use Cases
The Weekly Status Report
An Ops Engineer needs to compile a status report on 10 clients. Instead of running 10 separate reports and pasting data into a spreadsheet, they ask the agent to run list_onboarding_plans, then loop through each plan ID calling get_plan_progress_analytics for the metrics. The problem is solved in one prompt sequence.
The Stuck Task Investigation
A CS Lead notices a client's progress stalled. They ask the agent to check the tasks using list_plan_tasks, see that 'API Integration' is overdue, and then call get_task_details to find out who was assigned and why it failed.
The Project Scope Review
An Implementation Manager needs to know exactly what the original scope was. They ask for plan details using get_plan_details, then call list_plan_comments to review all prior discussions and agreements attached to that specific plan.
Staffing Conflict Check
The team is adding a new project but isn't sure who should be assigned. The manager uses list_team_members to see the available specialists, then checks get_member_details for credentials before assigning the role.
The Tradeoffs
Manual Dashboard Hopping
The user opens Onboard.io, filters by status 'In Progress', clicks on Client A, then copies 3 tasks' IDs to a spreadsheet, and repeats this process for five different clients.
→ Tell your AI agent to run list_onboarding_plans first. Then, ask the agent to loop through those plans, calling get_plan_progress_analytics and listing all associated tasks using list_plan_tasks. This gathers all the data in one go.
Guessing Plan IDs
The user remembers a plan existed but can't recall its specific ID, so they waste time searching through old emails and internal wikis to find it.
→ Don’t guess. Start by calling list_onboarding_plans to get the full list of IDs and names. Then use that ID in get_plan_details for guaranteed data retrieval.
Over-reliance on Chat Logs
The team assumes all decisions are logged in email chains, making it impossible to track official scope changes or task handoffs.
→ Always ask the agent to run list_plan_comments. This tool pulls the structured internal comments attached directly to the plan record, which is the single source of truth.
When It Fits, When It Doesn't
Use this server if your primary bottleneck is stitching together data from Onboard.io's multiple functional areas (tasks, plans, customers) into a coherent narrative or report. It shines when you need to answer 'What is the status?' by combining inputs like customer details AND plan analytics AND task assignments.
Don’t use this if: 1) You only need a single piece of data (e.g., just listing all team members—a simple API call suffices). 2) Your process flow requires complex, multi-step database writes or modifications (this is for reading/retrieving status). If you're managing tickets in Jira, use a dedicated ticketing integration instead; this tool is specific to Onboard.io.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Onboard.io. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Checking client status used to mean juggling five different tabs just to get one answer.
Today, getting a full picture of an onboarding project means logging into the main dashboard, checking the task list for due dates, then switching over to the customer profile page to see their stage. If you need history, you have to find the right plan ID and manually pull up the comments. It's slow, it requires copy-pasting IDs, and it’s easy to miss something crucial.
With this MCP server, your AI agent handles the context switching. You just ask: 'What's the health status for Global Tech?' The agent runs get_plan_details, checks get_plan_progress_analytics, confirms the tasks with list_plan_tasks, and gives you one clean answer. It’s instant.
Onboard.io Implementation MCP Server: Get all plan context in a single prompt.
Manual reporting requires running multiple queries—one for tasks, one for customers, and another for comments—and then stitching the data together yourself. Every missing ID or wrong scope means the report is inaccurate, forcing you to start over.
Now, your AI client coordinates it all. You ask a question that covers the entire project lifecycle, and the agent calls multiple tools like list_onboarding_plans, get_task_details, and list_plan_comments sequentially. The result is accurate, complete context on demand.
Common Questions About Onboard.io Implementation MCP
How do I check a plan's health score using the Onboard.io Implementation MCP Server? +
You use get_plan_progress_analytics to fetch this data. You just need the specific Plan ID, and the tool returns the calculated metrics like completion percentage or overall health rating.
Can I list all active plans using the Onboard.io Implementation MCP Server? +
Yes, use list_onboarding_plans. You can filter this list by status (like 'active') to quickly narrow down only the projects you need to review.
What is get_task_details used for in Onboard.io? +
get_task_details pulls everything about a single task: who owns it, when it's due, and its current status. This lets you verify if the assigned person still has ownership.
Do I need to know an ID to list customers using Onboard.io? +
No, use list_onboarding_customers. It retrieves a paginated list of all clients in the pipeline, so you don't have to track them manually.
What credentials do I need before running `list_onboarding_plans`? +
You must provide a valid API Key, which works as a Bearer Token. This key authenticates your agent against the Onboard.io platform so it knows who you are and what data you're allowed to see.
How does `list_plan_comments` handle discussion history? +
list_plan_comments pulls a chronological record of all internal notes and discussions for a given implementation plan ID. It lets your agent track who said what, and when, without you having to open the portal.
If I call `get_onboarding_customer_details`, what information should I expect? +
You get a complete profile dump for that customer account. This includes all foundational details about their status, contact info, and current place in the onboarding lifecycle.
When using `list_onboarding_customers`, what data points are returned? +
The tool returns a list of basic customer profiles, including identifiers and general status markers. You then use these IDs to call specific tools, like get_plan_details, for the deep context you need.
How do I get an Onboard.io API Key? +
You can find your API key in the Onboard.io dashboard under Settings > API. This token is used for Bearer authentication.
Can I see internal comments on a plan? +
Yes! Use the list_plan_comments tool with a specific Plan ID to retrieve the discussion history and internal notes for that implementation.
What progress metrics are available? +
The get_plan_progress_analytics tool provides health scores, percent complete, and overview stats for your launch plans.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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