What is Model Context Protocol (MCP) and Why It Matters for Teams

Model Context Protocol (MCP) is an open standard that enables AI assistants to connect directly to external tools and data sources. Instead of copying and pasting information, your AI assistant can read, create, and update data in real-time through a standardized interface.

Understanding MCP: Client vs Server

MCP operates on a client-server model:

MCP Client

The AI assistant application (like Claude Desktop, Cursor, or Windsurf) that makes requests:

Discovers available tools from connected servers
Sends tool calls based on user prompts
Receives and processes responses
Presents results to users in natural language

MCP Server

The service that provides tools and data (like Corcava's MCP server):

Exposes a set of tools (functions) that can be called
Provides resources (data) that can be accessed
Handles authentication and authorization
Executes operations and returns results

MCP Components: Tools, Resources, and Prompts

Tools

Tools are functions that the AI assistant can call to perform actions. For example, Corcava's MCP server provides tools like:

list_tasks - Search and filter tasks
create_task - Create new tasks
start_time_tracking - Start tracking time on a task
add_task_comment - Add comments to tasks

Resources

Resources are data sources that can be read (but not modified directly). They provide context to the AI assistant without requiring explicit tool calls.

Prompts

Prompts are reusable templates that help structure common interactions. They guide the AI assistant on how to use tools effectively for specific workflows.

When MCP is Better Than Copy/Paste

Traditional workflows require manual copying and pasting information between your project management tool and your AI assistant. MCP eliminates this friction:

❌ Without MCP

Copy task details manually
Paste into AI chat
AI responds with suggestions
Manually create/update tasks
Repeat for each task

✅ With MCP

Ask AI: "What tasks are due this week?"
AI reads directly from Corcava
AI provides analysis and suggestions
AI can create/update tasks directly
Everything happens in one conversation

Real Examples for Project Management

Example 1: Weekly Planning

You: "What tasks are due this week, and which ones are blocked?"

AI (via MCP):

Calls list_tasks with filters for due dates
Analyzes task statuses and dependencies
Identifies blocked tasks
Presents a prioritized weekly plan

Example 2: Task Creation from Meeting Notes

You: "Create tasks from these meeting notes: [pastes notes]"

AI (via MCP):

Extracts action items from notes
Calls create_task for each action item
Sets appropriate due dates and assigns owners
Links related tasks together

Example 3: Status Reporting

You: "Generate a status report for Project X"

AI (via MCP):

Calls get_project to get project details
Calls list_tasks filtered by project
Analyzes task completion rates and timelines
Generates a stakeholder-ready status report

How Corcava Fits as a Project Management MCP Server

Corcava provides a comprehensive MCP server specifically designed for project and work management. Our MCP server exposes tools for:

📋 Task Management

Create, read, update, and delete tasks with full context

📁 Project Organization

Access projects, boards, and organizational structure

⏱️ Time Tracking

Start, stop, and manage time tracking sessions

💬 Collaboration

Add comments, track decisions, and maintain audit trails

🔍 Search & Filter

Find tasks, projects, and boards with powerful filtering

🔒 Security

API key authentication with team-scoped access

Learn more about Corcava's MCP capabilities in our project management MCP server guide.

Frequently Asked Questions

Is MCP secure?

Yes. MCP uses API key authentication, and each request is scoped to your team's data only. You can revoke access at any time. Learn more in our MCP security guide.

Which AI assistants support MCP?

MCP is supported by Claude Desktop, Cursor, Windsurf, Continue, and other AI assistants. See our client setup guides for specific instructions.

Do I need to write code to use MCP?

No. Once configured, you interact with MCP through natural language with your AI assistant. The AI handles all tool calls automatically based on your requests.

Can MCP modify my data?

MCP tools can read and write data based on your API key permissions. You can use confirmation patterns to require approval before write operations. See our least-privilege workflows guide.

What's the difference between MCP and REST APIs?

MCP is designed for interactive AI assistants, while REST APIs are for direct application integrations. MCP provides standardized tool discovery, natural language interaction, and better error handling for AI contexts. Learn more in our comparison guide.

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