Taskray MCP Server

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A standardized, secure bridge between TaskRay and any AI platform — Agentforce, Claude, OpenAI, or your own agents. Real-time access. Full audit trail. Your governance.
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What It Does
The bridge between TaskRay and every AI platform
The Model Context Protocol (MCP) is an open standard that lets AI agents read, understand, and act on TaskRay data without building custom integrations. MCP securely reads your project data. Claude reasons about resource constraints. Your own agents execute work, all with the same permissions and audit trail as a human user.
Projects
plans · phases · milestones
Tasks
assignments · dependencies · checklists
Timelines
Gantt · schedules · due dates
Resources
capacity · allocation · roles
Delivery status
progress · at risk · blockers
Portfolios
health · rollups · reporting
TaskRay MCP
TaskRay
READ · REASON · ACT
Claude
Anthropic
ChatGPT
OpenAI
Agentforce
Salesforce
Copilot
Microsoft
Any MCP
Open standard
TaskRay MCP diagram connecting project, task, and resource data to ChatGPT, Claude, Agentforce, Gemini, and Copilot
Read
Query projects, tasks, timelines, resources, and delivery status. Any AI tool can understand your work in real-time.
Reason
Let AI agents make sense of complex interdependencies, risks, and opportunities in your delivery pipeline.
Act
Agents can update status, reassign resources, escalate risks — every action is logged and auditable.
WORK IN TASKRAY FROM MICROSOFT TEAMS AND SLACk
|
Ask Agents questions in Chat.
Get real answers from TaskRay.
Ask
Tag
Read
Analyze
Reply
Email
Microsoft Teams
ACME Onboarding — Delivery Team
MW
Marcus Webb 10:42 AM
Where are we on the ACME onboarding project? Exec review is Thursday.
PS
Priya Sharma 10:43 AM
@TaskRay give us a project summary — progress and any risks.
TaskRay Agent is reading the project plan
TR
TaskRay Agent AGENT
ACME Onboarding — status
68% complete — Discovery and Build closed on plan.
Risk: Data Security Questionnaire at 0% blocks UAT. 4 days of critical-path exposure.
Thursday call: go-live holds if the questionnaire lands by Tuesday.
MW
Marcus Webb 10:45 AM
Perfect. @TaskRay email that to the full project team as a pre-read for Thursday.
TR
TaskRay Agent AGENT
Sent via Outlook DELIVERED
ToACME Onboarding — Project Team (9)
SubjPre-read: ACME Onboarding status ahead of Thursday
Type a message
TaskRay / ACME — Onboarding
GANTT
Overall
68%
Critical path
−4 days
Open risks
2
Work hierarchy
ACME Onboarding
Discovery & Design
Requirements Gathering
Build & Configuration
Data Security Questionnaire
UAT & Validation
Go-Live
MAY
JUN
JUL
AUG
68%
100%
100%
82%
0%
RISK
0%
0%
TR
How It Works
The TaskRay MCP stack
Layer 1
TaskRay Core
Projects, tasks, resources, timelines, collaboration — the system of record.
Layer 2
MCP Protocol
Standard interface. Read, reason, act. Full audit trail.
Layer 3
Any AI Platform
Agentforce, Claude, OpenAI, Anthropic, your agents.
Layer 4
Real Results
Faster delivery. Smarter decisions. Predictable outcomes.
Limited ACCESS
Join TaskRay's MCP Beta
Connect your AI assistant to live delivery data before general availability.
  • Run TaskRay headless from Claude, GhatGPT, Copilot, Slack or Teams        
  • Free during the beta
Deploy immediately
Pre-built TaskRay agents
Agent
For Delivery Leaders, PMOs
Project Analyst
Real-time insights. Risk detection. Delivery forecasting. Escalations.
Agent
For Project Managers
Task Planner
Generates tasks from customer requirements. Schedules dependencies. Optimizes parallelization.
Agent
For Resource Managers
Resource Advisor
Identifies capacity gaps. Suggests reallocation. Prevents bottlenecks.
Why use TaskRay MCP
Enterprise-grade AI integration
Standardized Protocol
No custom integrations. One MCP, any AI platform.
Full Governance
Every AI action is logged. Same permissions as human users.
Real-time Data
Live project status, resource availability, delivery metrics.
Extensible
Build your own agents. Deploy to any LLM or AI platform.
Secure by Design
API keys, rate limiting, audit trails. Enterprise-ready.
Instant Start
Pre-built agents for common use cases. Ready in minutes.
Get Started
Everything You Need
API Documentation
Full API reference, code samples, best practices.
API Access
Get your keys and start building in the sandbox.
Getting Started Guide
Step-by-step walkthrough of your first MCP integration.

Ready to unlock AI for delivery?

Get API access today and start building with the TaskRay MCP.
Frequently Asked Questions

TaskRay MCP is a secure connectivity layer that allows compatible AI assistants and agents to access and interact with TaskRay project information.

Through TaskRay MCP, users can ask questions about projects, retrieve delivery insights, and initiate approved project actions from AI experiences such as ChatGPT, Claude, or other MCP-compatible tools.

MCP stands for Model Context Protocol. It is an open standard that helps AI applications connect with external tools and business systems.

TaskRay MCP makes TaskRay project context and approved project actions available to compatible agents without requiring every integration to be built independently.

Depending on the organization’s configuration and permissions, an agent may be able to:

  • Review project status
  • Automate project kickoffs
  • Retrieve tasks and milestones
  • Identify overdue or blocked work
  • Summarize project health
  • Find risks requiring attention
  • Assign project work to resources
  • Create or update approved project records
  • Prepare leadership briefings
  • Coordinate work from external AI interfaces

The specific actions available depend on the user’s access, the connected agent, and the organization’s governance policies.

TaskRay MCP is designed to work within the organization’s Salesforce security model. Agent access can reflect the permissions, sharing rules, and business controls assigned to the authenticated user.

This allows organizations to extend project access into AI experiences without creating a separate, uncontrolled source of project data.

Yes. Organizations can connect compatible agents and AI applications to TaskRay MCP.

Customers can use their preferred AI tools, deploy TaskRay Agent Packs, or work with TaskRay Services to design and configure custom agents around their delivery processes.

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