Task API Server - MCP TypeScript Implementation
LocalAbout Task API Server - MCP TypeScript Implementation
A TypeScript MCP server that connects to an external Task API for AI tooling and web clients. It supports both STDIO and HTTP+SSE, exposing task resources and operations.
Key features
- 01Filter tasks by status, category, and priority
- 02Create, update, and delete tasks
- 03STDIO and HTTP+SSE modes
- 04Expose task data structure resources
- 05Test client with response validation
Use cases
- 01Query and maintain tasks from AI tooling
- 02Access task operations through a browser client
- 03Find pending tasks by status or category
MCP support
The server exposes task data resources and tools for listing, creating, updating, and deleting tasks to MCP clients. It supports STDIO and HTTP+SSE transports.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMETask API Server - MCP TypeScript Implementation
A Model Context Protocol (MCP) implementation for Task Management API written in TypeScript. This project serves as both a reference implementation and a functional task management server.
Overview
This MCP server connects to an external Task API service and provides a standardized interface for task management. It supports two runtime modes:
- STDIO Mode: Standard input/output communication for CLI-based applications and AI agents
- HTTP+SSE Mode: Web-accessible server with Server-Sent Events for browser and HTTP-based clients
The server offers a complete set of task management operations, extensive validation, and robust error handling.
Features
-
Task Management Operations:
- List existing tasks with filtering capabilities
- Create new tasks with customizable properties
- Update task details (description, status, category, priority)
- Delete tasks when completed or no longer needed
-
Dual Interface Modes:
- STDIO protocol support for command-line and AI agent integration
- HTTP+SSE protocol with web interface for browser-based access
-
MCP Protocol Implementation:
- Complete implementation of the Model Context Protocol
- Resources for task data structures
- Tools for task operations
- Error handling and informative messages
-
Quality Assurance:
- Comprehensive test client for validation
- Automatic server shutdown after tests complete
- Detailed validation of API responses
Getting Started
Prerequisites
- Node.js 16.x or higher
- npm or pnpm package manager
Installation
-
Clone the repository:
git clone https://github.com/yourusername/mcp-template-ts.git cd mcp-template-ts -
Install dependencies:
npm installor using pnpm:
pnpm install -
Create an
.envfile with your Task API credentials:TASK_MANAGER_API_BASE_URL=https://your-task-api-url.com/api TASK_MANAGER_API_KEY=your_api_key_here TASK_MANAGER_HTTP_PORT=3000 -
Build the project:
npm run build
Running the Server
STDIO Mode (for CLI/AI integration)
npm start
or
node dist/index.js
HTTP Mode (for web access)
npm run start:http
or
node dist/http-server.js
By default, the HTTP server runs on port 3000. You can change this by setting the TASK_MANAGER_HTTP_PORT environment variable.
Testing
Run the comprehensive test suite to verify functionality:
npm test
This will:
- Build the project
- Start a server instance
- Connect a test client to the server
- Run through all task operations
- Verify correct responses
- Automatically shut down the server
Using the MCP Client
STDIO Client
To connect to the STDIO server from your application:
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
import * as path from 'path';
// Create transport
const transport = new StdioClientTransport({
command: 'node',
args: [path.resolve('path/to/dist/index.js')]
});
// Initialize client
const client = new Client(
{
name: "your-client-name",
version: "1.0.0"
},
{
capabilities: {
prompts: {},
resources: {},
tools: {}
}
}
);
// Connect to server
await client.connect(transport);
// Example: List all tasks
const listTasksResult = await client.callTool({
name: "listTasks",
arguments: {}
});
// Example: Create a new task
const createTaskResult = await client.callTool({
name: "createTask",
arguments: {
task: "Complete project documentation",
category: "Documentation",
priority: "high"
}
});
// Clean up when done
await client.close();
HTTP Client
To connect to the HTTP server from a browser:
<!DOCTYPE html>
<html>
<head>
<title>Task Manager</title>
<script type="module">
import { Client } from 'https://cdn.jsdelivr.net/npm/@modelcontextprotocol/sdk/dist/esm/client/index.js';
import { SSEClientTransport } from 'https://cdn.jsdelivr.net/npm/@modelcontextprotocol/sdk/dist/esm/client/sse.js';
document.addEventListener('DOMContentLoaded', async () => {
// Create transport
const transport = new SSEClientTransport('http://localhost:3000/mcp');
// Initialize client
const client = new Client(
{
name: "browser-client",
version: "1.0.0"
},
{
capabilities: {
prompts: {},
resources: {},
tools: {}
}
}
);
// Connect to server
await client.connect(transport);
// Now you can use client.callTool() for tasks
});
</script>
</head>
<body>
<h1>Task Manager</h1>
<!-- Your interface elements here -->
</body>
</html>
Available Tools
listTasks
Lists all available tasks.
const result = await client.callTool({
name: "listTasks",
arguments: {
// Optional filters
status: "pending", // Filter by status
category: "Work", // Filter by category
priority: "high" // Filter by priority
}
});
createTask
Creates a new task.
const result = await client.callTool({
name: "createTask",
arguments: {
task: "Complete the project report", // Required: task description
category: "Work", // Optional: task category
priority: "high" // Optional: low, medium, high
}
});
updateTask
Updates an existing task.
const result = await client.callTool({
name: "updateTask",
arguments: {
taskId: 123, // Required: ID of task to update
task: "Updated task description", // Optional: new description
status: "done", // Optional: pending, started, done
category: "Personal", // Optional: new category
priority: "medium" // Optional: low, medium, high
}
});
deleteTask
Deletes a task.
const result = await client.callTool({
name: "deleteTask",
arguments: {
taskId: 123 // Required: ID of task to delete
}
});
Environment Variables
| Variable | Description | Default |
|---|---|---|
| TASK_MANAGER_API_BASE_URL | URL for the external Task API | None (Required) |
| TASK_MANAGER_API_KEY | API key for authentication | None (Required) |
| TASK_MANAGER_HTTP_PORT | Port for the HTTP server | 3000 |
| PORT | Alternative port name (takes precedence) | None |
Project Structure
mcp-template-ts/
├── dist/ # Compiled JavaScript files
├── src/ # TypeScript source files
│ ├── index.ts # STDIO server entry point
│ ├── http-server.ts # HTTP+SSE server entry point
│ ├── test-client.ts # Test client implementation
├── .env # Environment variables
├── package.json # Project dependencies
├── tsconfig.json # TypeScript configuration
└── README.md # Project documentation
Development
-
Start the TypeScript compiler in watch mode:
npm run watch -
Run tests to verify changes:
npm test
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- This project uses the @modelcontextprotocol/sdk for MCP protocol implementation
- Built for integration with AI tooling and web applications
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.