How do I use MCP Utility Kit?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Utility Kit tell me a joke" 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 Utility Kit

by thananauto

Overview Schema Related Servers Score Discussions

Python

Remote

MCP Utility Kit

PyPI version Python 3.13+ License: MIT

An MCP (Model Context Protocol) server built with FastMCP that provides three useful daily utility tools:

Tools Provided

Random Joke - Get a random joke of the day
Weather Data - Get current weather using latitude and longitude
Age Prediction - Predict age based on a person's name

Related MCP server: Utility Tools MCP Server

Features

🎭 Daily jokes from the Official Joke API
🌤️ Real-time weather data from Open-Meteo
👤 Name-based age prediction from Agify
🚀 Fast and lightweight MCP server
📦 Published on PyPI - ready to use
⚡ No installation needed with uvx

Quick Start

No installation or cloning required! Just add to your MCP configuration and start using.

Prerequisites

uv package manager

Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

Installation & Setup

Quick Command (Claude CLI)

If you have the Claude CLI installed, add the server with one command:

claude mcp add daily-utils uvx mcp-utility-kit

Option 1: Direct Use with uvx (Recommended)

Use directly without installation:

Add this configuration to your MCP settings file:

VSCode: ~/Library/Application Support/Code/User/mcp.json (Mac) or %APPDATA%\Code\User\mcp.json (Windows)

Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json (Mac)

{
  "mcpServers": {
    "daily-utils": {
      "command": "uvx",
      "args": ["mcp-utility-kit"],
      "type": "stdio"
    }
  }
}

Then reload your MCP client:

VSCode: Press Cmd+Shift+P (Mac) or Ctrl+Shift+P (Windows) → "Developer: Reload Window"
Claude Desktop: Restart the application

That's it! The server will automatically download and run.

Option 2: Install via pip

If you prefer traditional installation:

pip install mcp-utility-kit

Then run directly:

python -m mcp_utility_kit

Option 3: Local Development Setup

For contributing or modifying the code:

Clone the repository:

git clone https://github.com/thananauto/mcp-utility-kit.git
cd mcp-utility-kit

Install dependencies:

uv sync

Use local configuration in mcp.json:

{
  "mcpServers": {
    "daily-utils": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/full/path/to/mcp-utility-kit",
        "python",
        "-m",
        "mcp_utility_kit"
      ],
      "type": "stdio"
    }
  }
}

Replace /full/path/to/mcp-utility-kit with your local clone path

Usage

Once configured in your MCP client, you can use these tools through your AI assistant:

"Tell me a joke" - Gets a random joke
"What's the weather in New York?" (provide latitude: 40.7128, longitude: -74.0060)
"Predict the age for the name Michael"

Running Standalone in Terminal

# Run directly with uvx (no install needed)
uvx mcp-utility-kit

# Or if installed via pip
python -m mcp_utility_kit

# From local development
uv run python -m mcp_utility_kit

Testing with MCP Inspector

The MCP Inspector provides a web UI for testing:

# Test published version
npx @modelcontextprotocol/inspector uvx mcp-utility-kit

# Test local version
npx @modelcontextprotocol/inspector uv run python -m mcp_utility_kit

This opens a browser interface where you can test all tools interactively.

Available Tools

1. get_joke_of_the_day()

Gets a random joke from the Official Joke API.

Returns: A formatted joke with setup and punchline

Example:

Why did the chicken cross the road?
To get to the other side!

2. get_weather(latitude: float, longitude: float)

Gets current weather data for a location.

Parameters:

latitude - Latitude of the location (e.g., 52.52 for Berlin)
longitude - Longitude of the location (e.g., 13.41 for Berlin)

Returns: Formatted weather summary with temperature, wind speed, humidity, and weather code

Example:

get_weather(latitude=40.7128, longitude=-74.0060)  # New York City

3. predict_age_by_name(name: str)

Predicts the age associated with a given name using the Agify API.

Parameters:

name - First name to predict age for (e.g., "Michael", "Sarah")

Returns: Predicted age and confidence count

Example:

predict_age_by_name(name="Michael")

APIs Used

This server integrates with the following free APIs:

Official Joke API - Random jokes
Open-Meteo Weather API - Weather data (no API key required)
Agify Age Prediction API - Name-based age prediction

Updating the Package

This package is published on PyPI at: https://pypi.org/project/mcp-utility-kit/

Publishing Updates

To publish a new version:

Update version in pyproject.toml:

version = "0.2.0"  # Increment version number

Build the package:

uv build

Install publishing tools (if not already installed):

uv pip install twine

Upload to PyPI:

uv run twine upload -u __token__ -p YOUR_PYPI_TOKEN dist/*

Get your PyPI token from: https://pypi.org/manage/account/token/

Test Before Publishing

Test on Test PyPI first (optional):

uv run twine upload --repository testpypi -u __token__ -p YOUR_TEST_TOKEN dist/*

Team members can use this server immediately with no installation:

1. Install uv (if needed)

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Add MCP Configuration

Add to mcp.json (VSCode) or claude_desktop_config.json (Claude Desktop):

{
  "mcpServers": {
    "daily-utils": {
      "command": "uvx",
      "args": ["mcp-utility-kit"],
      "type": "stdio"
    }
  }
}

3. Reload MCP Client

That's it! No cloning, no manual installation needed.

Package Link: https://pypi.org/project/mcp-utility-kit/ Repository: https://github.com/thananauto/mcp-utility-kit

Project Structure

mcp-utility-kit/
├── mcp_utility_kit/
│   ├── __init__.py       # Package initialization
│   ├── server.py         # MCP server implementation with tools
│   └── __main__.py       # Entry point
├── pyproject.toml        # Project configuration and dependencies
├── uv.lock              # Dependency lock file
└── README.md            # This file

Development

Built With

FastMCP - Framework for building MCP servers
httpx - Async HTTP client for API requests
uv - Fast Python package manager

Adding New Tools

To add a new tool to the server:

Open mcp_utility_kit/server.py
Add a new function decorated with @mcp.tool():

@mcp.tool()
async def your_new_tool(param: str) -> str:
    """Tool description for LLM context.

    Args:
        param: Parameter description

    Returns:
        What the tool returns
    """
    # Your implementation here
    return "result"

Test locally with MCP Inspector
Rebuild and republish if deploying to PyPI

Troubleshooting

Server Won't Start

Check uv is installed:
```
uv --version
```
If not installed: curl -LsSf https://astral.sh/uv/install.sh | sh
Test the server directly:
```
uvx mcp-utility-kit
```
Check MCP configuration: Ensure mcp.json has the correct format (see Installation section)
View logs: In VS Code, open Output panel (View → Output) and select "MCP" from dropdown

Tools Not Appearing

Reload MCP client: In VS Code, press Cmd+Shift+P → "Developer: Reload Window"
Check server status: Look for "daily-utils" in the MCP Output logs
Verify configuration: Double-check the JSON syntax in your mcp.json

Package Version Issues

To force update to the latest version:

uvx --refresh mcp-utility-kit

API Errors

Check internet connection: All three APIs require internet access
Rate limits: Free tier APIs may have rate limits
Regional restrictions: Verify APIs are accessible from your location

Getting Help

Check logs: VS Code Output panel → MCP section shows detailed error messages
GitHub Issues: https://github.com/thananauto/mcp-utility-kit/issues
MCP Documentation: https://modelcontextprotocol.io/

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions:

Open an issue in the repository
Check existing issues for solutions
Review the MCP documentation

Available Tools

3 tools

get_joke_of_the_dayA

Get a random joke from the official joke API.

Returns: A formatted joke with setup and punchline.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.1/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided; description mentions return format (setup and punchline) but lacks details on behavior like idempotency or performance.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences; front-loaded with action; no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Low complexity, no parameters, output schema exists; description covers return format, sufficient for agent usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist, baseline score 4; description adds no parameter info but none is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'random joke', and distinct from sibling tools get_weather and predict_age_by_name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives, but sibling tools are unrelated, making usage obvious.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_weatherA

Get current weather data for a location using latitude and longitude.

Args: latitude: Latitude of the location (e.g., 52.52 for Berlin) longitude: Longitude of the location (e.g., 13.41 for Berlin)

Returns: Formatted weather summary with temperature, wind speed, and humidity.

ParametersJSON Schema

Name	Required	Description	Default
`latitude`	Yes
`longitude`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It adequately describes the return format (formatted weather summary with temperature, wind speed, humidity) and provides example values. It is transparent about being a read operation, though it does not cover error handling or units.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: a single paragraph with clear 'Args' and 'Returns' sections, front-loaded with purpose. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With a simple input schema (2 required numbers) and the presence of an output schema, the description covers the essential information. It effectively explains what the tool does and what it returns, making it complete for agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, meaning the schema provides no descriptions. The description adds specific examples (e.g., 52.52 for Berlin) for latitude and longitude, adding meaningful context beyond the schema's type and title.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it retrieves current weather data using latitude and longitude, which is a specific verb+resource, and distinguishes well from unrelated sibling tools (get_joke_of_the_day, predict_age_by_name).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs alternatives. However, the sibling tools are unrelated, so usage context is implicitly clear, but no exclusions or alternatives are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

predict_age_by_nameA

Predict the age associated with a given name using the Agify API.

Args: name: The first name to predict age for (e.g., "Michael", "Sarah")

Returns: Predicted age and confidence count for the name.

ParametersJSON Schema

Name	Required	Description	Default
`name`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.5/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, description must disclose behavioral traits. It mentions Agify API but no limitations, rate limits, accuracy, or data scope (e.g., US-centric). Basic functionality only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Succinct two-sentence description plus Args/Returns. No fluff, but could be even more compact (e.g., remove 'using the Agify API' as it's not critical).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Simple tool with one parameter; description covers purpose, input, and output. Output schema exists, so return values are explained. Lacks caveats about prediction reliability.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must add meaning. It provides examples ('Michael', 'Sarah') and specifies 'first name', but lacks format constraints or additional context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the action ('Predict the age') and the resource ('associated with a given name'). Sibling tools are unrelated (joke, weather), so no ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use or when not to use. The purpose is implied but no alternatives or exclusions mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

3 tool updatesv0.1.0Jul 29, 2026
- First observedget_joke_of_the_day
- First observedget_weather
- First observedpredict_age_by_name

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.1/5.0

Disambiguation5/5

Each tool serves a completely distinct purpose: jokes, weather, and age prediction. There is no overlap in functionality or intent.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with 'get_' prefix and snake_case, e.g., get_joke_of_the_day, get_weather, predict_age_by_name. The pattern is uniform.

Tool Count5/5

With 3 tools, the count is well-scoped for a utility kit offering a few unrelated but useful functionalities. It is neither too few nor too many.

Completeness4/5

For a miscellaneous utility kit, the tools cover common requests (joke, weather, age prediction). Minor gaps exist (e.g., no random number generator), but the surface is reasonable for the domain.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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MCP Utility Kit

Tools Provided

Features

Quick Start

Prerequisites

Installation & Setup

Quick Command (Claude CLI)

Option 1: Direct Use with uvx (Recommended)

Option 2: Install via pip

Option 3: Local Development Setup

Usage

Running Standalone in Terminal

Testing with MCP Inspector

Available Tools

1. get_joke_of_the_day()

2. get_weather(latitude: float, longitude: float)

3. predict_age_by_name(name: str)

APIs Used

Updating the Package

Publishing Updates

Test Before Publishing

Sharing with Team Members

1. Install uv (if needed)

2. Add MCP Configuration

3. Reload MCP Client

Project Structure

Development

Built With

Adding New Tools

Troubleshooting

Server Won't Start

Tools Not Appearing

Package Version Issues

API Errors

Getting Help

License

Contributing

Support

Available Tools

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Latest Blog Posts

MCP directory API