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A couple of months ago, we launched Rocketlane MCP in beta, giving teams an early look at what it means for AI to actually work inside your delivery operations, not just alongside them.
From all the customer interviews since, one thing became clear: this isn't a nice-to-have, it's a must-have. Teams told us it has extensively helped them focus more on client outcomes and less on manual admin.
Today, we're launching Rocketlane MCP into General Availability.
Every AI tool your team already uses can now read and act on your Rocketlane data directly. Projects, tasks, timesheets, templates, the full context of your delivery operation, available to your AI the moment you need it.
Rocketlane MCP ships with 24 tools today:
Read
get_my_profileget_projectsget_tasksget_usersget_phasesget_fieldsget_customer_companiesget_time_entriesget_time_entry_categoriesget_conversationsget_project_messagesget_task_messagesWrite
create_phaseupdate_phasecreate_taskupdate_taskcreate_time_entryupdate_time_entryupdate_projectcreate_project_templatesend_project_messagesend_task_messageupdate_project_messageupdate_task_message
And we have more on the pipeline, releasing soon to make your workflows even faster and more efficient.
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Timesheet entry represents a persistent friction point. Teams understand their work but resist manual logging.
Combined with Google Calendar MCP, the AI reviews your calendar, identifies meetings and work sessions, matches them to active Rocketlane projects, and generates corresponding time entries.
Alternatives include uploading activity CSVs or describing your week conversationally: "I spent Tuesday and Wednesday on the Acme onboarding, about 6 hours each day."
Example prompt: "Look at my calendar from this week and log my time against my active Rocketlane projects."

Starting the week usually means clicking through project after project to piece together what is due, what slipped, and what needs attention first. That overhead adds up before the real work even begins.
Ask your AI for a single rundown across all your active projects. It pulls your projects and tasks, surfaces what is due this week, flags anything overdue or at risk, and hands you a plain-language summary you can act on right away.
Particularly useful first thing Monday, or before a team standup, when you need the full picture in seconds instead of minutes of digging through the app.
Example prompt: "Give me a Monday morning rundown of everything I own this week across my Rocketlane projects. Flag anything due, overdue, or at risk."
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Action items get buried in Slack threads, customer emails, and meeting notes, and someone still has to translate them into Rocketlane by hand. The context lives in one place, the work lives in another.
Because Rocketlane MCP works alongside the other connectors your AI already has, it can read that context wherever it sits, a Slack th
read, an email, a set of notes, and create the matching tasks and phases directly in the right Rocketlane project. Drop a single task into a phase, or spin up a whole phase with its task list, all from the conversation you are already in.
No retyping, no tab switching. The follow-ups from your call are tasks in Rocketlane before the call even ends.
Example prompt: "Read the #acme-onboarding Slack thread and create tasks in the Acme project for every action item, grouped under a new Follow-ups phase."

Every PS team shapes Rocketlane around its own data model: project health, SOW expiry dates, savings delivered, risk reasons, external ticket IDs. That data is gold for reporting, but it lives scattered across projects and is not always easy to search or roll up natively.
Your AI can read any custom field through the MCP. Ask it to roll up project health across every active project, find the one task tied to a specific external ticket ID that native search will not surface, or pull every project whose SOW expires this quarter. When something needs correcting, it can update those field values for you too.
This is how teams turn the fields they already maintain into instant answers, without building a single report.
Example prompt: "Find the task where the Zendesk Ticket ID is 239 and tell me its project, status, and owner. If it is still open, mark the Ticket Status as solved."

Rocketlane MCP is more than a way to chat with your project data. It is a connection point. Once your delivery data is reachable through an open standard, it can flow into the other tools your business already runs on, and those tools can act on it.
Pipe project and time data into Snowflake for warehousing and reporting. Surface delivery metrics in Mixpanel next to product usage. Push status into Slack where your team already works. Connect the BI, data, and SaaS tools across your stack, and let them read from and write back to Rocketlane.
Because everything speaks the same open standard, your team can build agents and automated workflows on top of your delivery data, the kind that quietly handle the repetitive work so your PS team can spend its time on client outcomes instead of admin.
Rocketlane MCP works with Claude, Claude Code, and most AI tools that support MCP. Follow the step-by-step setup guide for your tool:
Guides for more tools are coming soon.
“Speeds up CSV importing and saves me from having to get customers to use a template file or create mapped data exports. Quick to integrate and flexible outside the happy path. We found defining workbooks and templates confusing; at a prior job it was configured through code, which I preferred.”
Source: G2 review


AI that executes your delivery work (Add to any plan)
Most popular
Ideal for expanding organizations needing more in-depth capabilities and integration for scaling.
Most popular
Great for teams desiring tailored workflows with comprehensive reporting capabilities.
Most popular
Tailored for large enterprises requiring a fully customizable, comprehensive delivery engine.

A Forward Deployed Engineer (FDE) embeds in the customer environment to implement, customize, and operationalize complex products. They unblock integrations, fix data issues, adapt workflows, and bridge engineering gaps — accelerating onboarding, adoption, and customer value far beyond traditional post-sales roles.





70–85% utilization. 94% G2 rating.
One platform does what the entire table above tries
to split across tools.
70–85% utilization. 94% G2 rating.
One platform does what the entire table above tries
to split across tools.

70–85% utilization. 94% G2 rating.
One platform does what the entire table above tries
to split across tools.
Enterprise implementations fail because customers don’t follow the process or provide clean data on time. Most delays are purely “customer-side” issues.
Implementations fail because complex environments need real-time technical problem-solving. FDEs unblock workflows, integrations, and unknown constraints that traditional onboarding teams can’t resolve on their own.
Get a better all-in-one PSA
Get a better all-in-one PSA
Companies that embed engineers directly with customers see significantly higher enterprise retention compared to traditional post-sales models — because embedded engineers uncover “unknowns” that never surface in ticket queues.

VP Sales, Intercom

A Forward Deployed Engineer (FDE) embeds in the customer environment to implement, customize, and operationalize complex products. They unblock integrations, fix data issues, adapt workflows, and bridge engineering gaps — accelerating onboarding, adoption, and customer value far beyond traditional post-sales roles.






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