wrike · MCP Server

Wrike + Improvado MCP — Project Visibility Without the Status Meeting

Improvado connects Wrike to Claude, ChatGPT, and any MCP-compatible AI agent. Query project health, task completion rates, team workload, and time tracking data in plain English — no manual report exports required.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Read: Get Any Wrike Metric Without Building Reports

Stop manually navigating Wrike dashboards and exporting reports. Ask your AI agent for overdue tasks by owner, project completion rates, workload distribution, or billable hour summaries — across any folder, project, or time period.

Example prompts

"Which projects have tasks overdue by more than 5 days? List by project and responsible team member."

25 min → 20 sec

"Show me workload distribution across the team for the current week. Who's over 100% capacity?"

20 min → 15 sec

"Compare Q1 vs Q2 project completion rates by department. Which departments are trending behind?"

1.5 hrs → 2 min

Works with Claude ChatGPT Cursor +5

Write

Write: Update Tasks and Projects Through Conversation

Your AI agent doesn't just read Wrike data — it acts on it. Update task statuses, reassign work, create subtasks from templates, and bulk-edit project timelines through natural language.

Example prompts

"Move all tasks in the 'Design Review' phase that have been waiting more than 3 days to 'Needs Attention' status."

20 min → 30 sec

"Reassign all open tasks from a team member going on leave to the designated backup for the next two weeks."

25 min → 1 min

"Create a new project from the Q3 Campaign template. Set start date to next Monday and assign to the content team."

20 min → 5 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Track Project Health Without Weekly Check-ins

Set up watches on the project metrics that matter. Your AI monitors Wrike continuously and flags overdue task accumulation, workload imbalances, and projects at risk of missing their deadline.

Example prompts

"Alert me when any project's overdue task count exceeds 10% of its total task count."

Manual → auto

"Every Friday at 4pm: send a project health digest — completion rates, overdue counts, and projects at risk."

1.5 hrs → auto

"Flag team members with more than 120% capacity utilization for 3 consecutive days."

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Status Reports Still Require Manual Assembly

The problem

Weekly project status reports require opening every active project in Wrike, checking task completion percentages, summarizing blockers, and pasting everything into a doc. With 15+ active projects, this takes 2+ hours every Friday — time that adds zero value.

How MCP solves it

Improvado extracts project and task data from Wrike into a queryable model. The MCP server lets your AI generate a complete status report in seconds — completion rates, overdue tasks, owner summaries, and risk flags across all projects.

Try asking

Generate a project status report for all active projects: completion rate, overdue tasks, and any projects flagged at risk.

Answer in seconds

All data sources, one query

Challenge 2

Workload Visibility Is Reactive

The problem

Over-allocation isn't visible until a team member misses a deadline or raises it in a 1:1. Wrike has workload views, but manually checking each person's capacity and comparing it to upcoming task due dates across multiple projects isn't done proactively.

How MCP solves it

Improvado normalizes Wrike task assignment and estimated effort data into a structured model. The MCP server lets your AI surface workload imbalances before they cause delays — flagging over-allocated team members and suggesting rebalancing.

Try asking

Who on the team is over-allocated this week? What tasks could be redistributed to team members with available capacity?

Full detail preserved

No data loss on export

Challenge 3

Time Tracking Data Is Rarely Analyzed

The problem

Wrike captures time entries, but almost nobody analyzes them. The data sits unused because turning it into meaningful insights — billable hours by project, estimated vs actual effort, most time-intensive task types — requires custom report building.

How MCP solves it

Improvado normalizes Wrike time tracking data alongside project and task metadata. The MCP server lets your AI analyze actual vs estimated hours by project, identify consistently underestimated task types, and surface billable hour summaries on demand.

Try asking

Compare estimated vs actual hours for completed projects in Q2. Which project types consistently run over estimate?

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What Wrike data can I access through the MCP server?

Tasks (status, assignee, due date, custom fields, subtasks), projects and folders, time tracking entries, workload and capacity data, project templates, comments, and attachments metadata. Both Wrike Business and Wrike Enterprise configurations are supported.

Can the AI write back to Wrike or just read data?

Both. Read operations include all project, task, and time tracking analytics. Write operations include creating and updating tasks, reassigning work, changing statuses, creating projects from templates, and managing subtasks. Permissions are scoped to your Wrike API credentials.

Does this work with Wrike's custom fields and workflows?

Yes. The MCP server preserves custom field values and workflow status configurations when extracting Wrike data. Your AI can query by custom field values, filter by workflow stage, and update custom fields through write operations — all through Improvado's hosted MCP server.

Which AI tools work with this Wrike MCP server?

Any MCP-compatible client — Claude Desktop, ChatGPT, Cursor, Windsurf, Gemini, and custom applications using MCP HTTP transport. Claude is the most commonly used due to native MCP support — all through Improvado's hosted MCP server.

How does this compare to Wrike's built-in analytics?

Wrike's native analytics are dashboard-based and require you to know which report to build. The MCP server lets you ask any question in plain language, combine Wrike data with data from other tools, and surface insights that aren't available in Wrike's reporting UI — all through Improvado's hosted MCP server.

How long does setup take?

If you're already an Improvado user, connect Wrike in the integrations panel and start querying at app.improvado.io/agent. For Claude Desktop or Cursor, add one line to your MCP config — under 60 seconds.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms