mcp-tasks-server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-tasks-serverAdd a task to prepare slides for tomorrow's meeting"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-tasks-server
A small Model Context Protocol server that exposes a personal task tracker to any MCP-compatible client (Claude Desktop, Claude Code, custom agents). Tasks live in a local SQLite database; no cloud, no surprises.
This is a reference implementation of an MCP server that takes real write-actions on persistent state — the kind of integration you build between an agentic AI platform and an enterprise system, scaled down to a laptop.
Tools exposed
Tool | Effect on state |
| Insert a new task |
| Read; optional status filter |
| Update |
| Hard-delete a task |
| Read; aggregate counts |
Each tool has a JSON schema declared in code, validated on every call.
Related MCP server: MCP Todo.txt Integration
Quick start
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python server.pyWire into Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"tasks": {
"command": "python",
"args": ["/absolute/path/to/server.py"]
}
}
}Then restart Claude Desktop. The tools appear in the MCP picker.
Sandbox / safety notes
Database file lives at
~/.local/share/mcp-tasks/tasks.dbby default; override withMCP_TASKS_DB. The path is normalized and confined to the user's home directory.delete_taskrequires the task ID to be an integer; no SQL is built from string concatenation. All queries use parameterized statements.The server has no network listeners — communication is over stdio per the MCP spec.
Sample tool calls
The full request/response shapes for every tool are in
examples/tool-call-examples.md.
Highlights:
Tool | Example call | Example result |
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Design notes
Why SQLite? Embedded, transactional, present on every Python install. A real enterprise integration would target a service API; the contract on the agent side is identical.
Why explicit JSON schemas? Some MCP clients display the schemas to the user before approving a tool call. Putting them inline in the server makes tool surfaces self-documenting and gives the LLM unambiguous parameter contracts.
Why no auth? Personal/local server. For an enterprise MCP server you'd add OIDC token verification at the tool boundary and scope each tool to the calling principal's permissions.
License
MIT — see LICENSE.
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