Todos MCP Server

What does “todos” MCP Server do?

The “todos” MCP Server is a todo list application that implements the Model Context Protocol (MCP), enabling seamless interaction between AI assistants and the application’s task management features. By exposing a standardized MCP-compliant API, this server allows AI models and chatbots to perform actions such as creating, reading, updating, and deleting tasks using natural language commands. Its MCP integration makes it possible for developers and users to manage tasks programmatically or via AI workflows, without requiring a SaaS account or external service. The server uses local storage for data persistence, emphasizing privacy and ease of use while serving as a practical demonstration of MCP capabilities in a real-world productivity tool.

List of Prompts

No specific prompt templates are mentioned in the available repository content.

List of Resources

No explicit list of MCP resources is provided in the repository documentation.

List of Tools

• Get-Todo: Retrieve a specific todo item by its ID.
• List-All-Todos: List all todo items.
• List-Completed-Todos: Display all completed tasks.
• List-Pending-Todos: Show all pending (incomplete) tasks.
• Add-Todo: Add a new todo item.
• Mark-Todo-Done: Mark a specified todo as completed.
• List-Due-Today: List all pending tasks due today or overdue.
• List-Due-This-Week: List all pending tasks due this week or earlier.
• Update-Todo: Update the description and/or due date of a todo item.

Use Cases of this MCP Server

• Personal Task Management: Use AI assistants to add, update, or complete personal tasks through natural language or automation, keeping track of your daily to-dos efficiently.
• Collaborative Workflows: Integrate with AI chatbots for team environments, allowing members to query, update, or assign todos without leaving their chat interface.
• Productivity Automation: Automate recurring or deadline-driven tasks by leveraging AI to list due tasks or create reminders.
• Context-aware Assistance: Enable AI models to suggest or manage todos based on user conversation context or project status.
• AI-powered Task Summarization: Allow LLMs to summarize pending, completed, or overdue tasks for quick status reporting.

How to set it up

Windsurf

No setup instructions provided for Windsurf in the repository.

Claude

1. Open your Claude configuration file, typically at ~/Library/Application Support/Claude/claude_desktop_config.json on MacOS.
2. Locate the mcpServers object in your config.
3. Add the todos MCP Server entry as follows:

   {
     "mcpServers": {
       "todos": {
         "command": "npx",
         "args": ["-y", "todos-mcp"]
       }
     }
   }
   

4. Save the file and restart Claude.
5. Verify that the server appears and is accessible in your Claude environment.

Cursor

No setup instructions provided for Cursor in the repository.

Cline

No setup instructions provided for Cline in the repository.

Securing API Keys

No information on securing API keys or use of environment variables is provided in the repository.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "todos": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “todos” to the actual name of your MCP server and replace the URL with your own MCP server URL.

Overview

Based on the provided information, the “todos” MCP Server offers a clear overview and toolset but lacks documentation on resources, prompt templates, API key security, and MCP features like roots or sampling.

Our opinion

The repository effectively demonstrates MCP tool integration for task management but lacks depth in documentation for prompts, resources, and advanced MCP features. Its setup instructions are limited to Claude, with no mention of other platforms. Overall, it serves as a good starting point for MCP-enabled apps but would benefit from expanded documentation and best practices.

MCP Score

Rating: 4/10
Reason: Solid basic MCP demo with good tool support, but limited documentation and ecosystem integration reduce its score.