How to Connect AI Agents to Stellary via MCP
A practical guide to integrating AI agents with your Stellary workspace using the Model Context Protocol.
Mar 28, 20263 min read
If you've been following the AI tooling space, you've probably heard about MCP — the Model Context Protocol. But what is it exactly, and why should you care?
What Is MCP?
The Model Context Protocol is an open standard that defines how AI models communicate with external tools and data sources. Think of it as USB for AI: a universal connector that lets any AI agent plug into any compatible tool without custom integration code.
Before MCP, connecting an AI agent to your project management tool, your code editor, or your documentation system required building custom integrations for each combination. With MCP, you build one integration, and every MCP-compatible AI agent can use it.
How MCP Works
The Architecture
MCP follows a client-server model:
- MCP Client — the AI agent (Claude, GPT, or any compatible model)
- MCP Server — the tool that exposes its capabilities (your PM tool, IDE, database, etc.)
- Protocol — the standardized communication layer between them
Tools, Resources, and Prompts
MCP servers expose three types of capabilities:
- Tools — actions the AI can perform (create a card, update a status, send a notification)
- Resources — data the AI can read (project board, documents, team members)
- Prompts — pre-defined interaction patterns (summarize this project, analyze this sprint)
The Communication Flow
When an AI agent connected via MCP needs to interact with your tool:
- The agent discovers available tools and resources from the MCP server
- The agent reads relevant resources to understand context
- The agent proposes an action using one of the available tools
- The action is executed (with human approval if configured)
- The result is returned to the agent
Why MCP Matters
No More Vendor Lock-In
Without MCP, you're tied to whichever AI your tool supports natively. With MCP, you can switch AI agents freely — from Claude to GPT to a custom model — without changing your tool integrations.
Richer AI Context
The biggest limitation of AI assistants today is lack of context. An AI that can only see the text you paste into a chat window is severely limited. MCP gives AI agents deep access to your actual work context — your board state, your documents, your team's priorities.
Composable AI Workflows
MCP enables AI agents to chain together multiple tools. An agent can read your project board, analyze your documentation, check your deployment status, and propose a coherent action plan — all through standardized MCP connections.
Lower Integration Cost
Building a custom AI integration typically requires weeks of engineering work. Exposing an MCP server takes a fraction of that time, and the result works with every MCP-compatible agent — not just one.
MCP in Practice
Project Management
An AI agent connected to your PM tool via MCP can work with your board to:
- Read your board to understand current sprint status
- Analyze velocity trends across past sprints
- Propose task reassignments when someone is overloaded
- Draft status updates based on actual card movements
- Flag blocked items that need attention
Documentation
Connected to your knowledge base, an AI agent can:
- Search documentation for relevant context
- Identify outdated docs that reference changed APIs
- Draft new documentation based on code changes
- Suggest related docs when you're working on a feature
Code Development
In your IDE, an MCP-connected agent can:
- Understand your project structure and conventions
- Access your team's coding standards
- Read related documentation while generating code
- Create cards for discovered issues or TODOs
Getting Started with MCP
As a User
If your tools already support MCP, getting started is straightforward:
- Generate an API token in your tool
- Configure your AI agent's MCP settings with the server URL and token — see our MCP integration guide for a step-by-step walkthrough
- Start interacting — the agent will discover available tools automatically
As a Developer
If you're building a tool and want to add MCP support:
- Implement the MCP server specification
- Define your tools (actions), resources (data), and prompts (patterns)
- Handle authentication and authorization
- Document your MCP capabilities
The MCP specification is open source, and SDKs are available for most languages.
The Future of MCP
MCP is rapidly becoming the standard protocol for AI-tool integration. As adoption grows, we expect to see:
- Universal AI assistants that can connect to all your work tools through a single protocol
- MCP marketplaces where tools publish their MCP capabilities
- Standardized permission models for controlling what AI agents can do
- Cross-tool workflows where AI orchestrates actions across multiple MCP-connected tools
The teams and tools that adopt MCP early are positioning themselves at the center of the AI-native workflow ecosystem.