mhlabs-mcp-tools
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., "@mhlabs-mcp-toolspreprocess the following text: remove stopwords and lemmatize"
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.
mhlabs-mcp-tools
mcp-name: io.github.MusaddiqueHussainLabs/mhlabs_mcp_tools
🧠 mhlabs-mcp-tools
mhlabs-mcp-tools is a Modular MCP Tools Server built using FastMCP.
It provides an extendable AI tool ecosystem organized into functional categories (Text Preprocessing, NLP Components, Document Analysis, etc.) that can be dynamically loaded and served through MCP (Model Context Protocol) via STDIO transport.
This project is part of the MHLabs AI Agentic Ecosystem, designed to work with mhlabs-mcp-server, mhlabs-mcp-agents, and downstream A2A agent frameworks.
Features
FastMCP Server: Pure FastMCP implementation supporting multiple transport protocols
Factory Pattern: Reusable MCP tools factory for easy service management
Domain-Based Organization: Services organized by business domains (HR, Tech Support, etc.)
Authentication: Optional Azure AD authentication support
Multiple Transports: STDIO, HTTP (Streamable), and SSE transport support
VS Code Integration: Debug configurations and development settings
Comprehensive Testing: Unit tests with pytest
Flexible Configuration: Environment-based configuration management
Architecture
mhlabs_mcp_tools/
├── .gitignore
├── .vscode/
│ └── settings.json
├── CHANGELOG.md
├── LICENSE
├── README.md
├── docs/
│ └── index.md
├── examples/
│ ├── example_client.py
│ └── example_client_http.py
├── mkdocs.yml
├── pyproject.toml
├── requirements.txt
├── server.json
└── src/
├── __init__.py
├── main.py
└── mhlabs_mcp_tools/
├── __init__.py
├── core/
│ ├── __init__.py
│ ├── config.py
│ ├── constants.py
│ ├── factory.py
│ └── prompts.py
├── data/
│ ├── __init__.py
│ ├── external/
│ │ └── __init__.py
│ ├── interim/
│ │ └── __init__.py
│ ├── processed/
│ │ └── __init__.py
│ └── raw/
│ ├── __init__.py
│ ├── contractions_dict.json
│ ├── custom_substitutions.csv
│ ├── leftovers_dict.json
│ └── slang_dict.json
├── handlers/
│ ├── __init__.py
│ ├── custom_exceptions.py
│ └── output_generator.py
├── mcp_server.py
├── models/
│ └── __init__.py
├── nlp_components/
│ ├── __init__.py
│ └── nlp_model.py
├── services/
│ ├── __init__.py
│ ├── langchain_framework.py
│ └── spacy_extractor.py
└── text_preprocessing/
├── __init__.py
├── contractions.py
├── emo_unicode.py
├── slang_text.py
└── text_preprocessing.pyAvailable Services
Currently the package is organized into three primary modules:
1. NLP Components
Component Type | Description |
tokenize | Text tokenization |
pos | Part-of-Speech tagging |
lemma | Word lemmatization |
morphology | Study of word forms |
dep | Dependency parsing |
ner | Named Entity Recognition |
norm | Text normalization |
2. Text Preprocessing
This module equips users with an extensive set of text preprocessing tools:
Function | Description |
to_lower | Convert text to lowercase |
to_upper | Convert text to uppercase |
remove_number | Remove numerical characters |
remove_itemized_bullet_and_numbering | Eliminate itemized/bullet-point numbering |
remove_url | Remove URLs from text |
remove_punctuation | Remove punctuation marks |
remove_special_character | Remove special characters |
keep_alpha_numeric | Keep only alphanumeric characters |
remove_whitespace | Remove excess whitespace |
normalize_unicode | Normalize Unicode characters |
remove_stopword | Eliminate common stopwords |
remove_freqwords | Remove frequently occurring words |
remove_rarewords | Remove rare words |
remove_email | Remove email addresses |
remove_phone_number | Remove phone numbers |
remove_ssn | Remove Social Security Numbers (SSN) |
remove_credit_card_number | Remove credit card numbers |
remove_emoji | Remove emojis |
remove_emoticons | Remove emoticons |
convert_emoticons_to_words | Convert emoticons to words |
convert_emojis_to_words | Convert emojis to words |
remove_html | Remove HTML tags |
chat_words_conversion | Convert chat language to standard English |
expand_contraction | Expand contractions (e.g., "can't" to "cannot") |
tokenize_word | Tokenize words |
tokenize_sentence | Tokenize sentences |
stem_word | Stem words |
lemmatize_word | Lemmatize words |
preprocess_text | Combine multiple preprocessing steps into one function |
Quick Start
Development Setup
Clone and Navigate:
cd src/mhlabs_mcp_toolsInstall Dependencies:
pip install -r requirements.txtConfigure Environment:
cp .env.example .env # Edit .env with your configurationStart the Server:
# Default STDIO transport (for local MCP clients) python mcp_server.py # HTTP transport (for web-based clients) python mcp_server.py --transport http --port 9000 or after installed mhlabs-mcp-tools python -m mhlabs_mcp_tools.mcp_server --transport http --port 9000 # Using FastMCP CLI (recommended) fastmcp run mcp_server.py -t streamable-http --port 9000 -l DEBUG # Debug mode with authentication disabled python mcp_server.py --transport http --debug --no-auth
Transport Options
1. STDIO Transport (default)
🔧 Perfect for: Local tools, command-line integrations, Claude Desktop
🚀 Usage:
python mcp_server.pyorpython mcp_server.py --transport stdio
2. HTTP (Streamable) Transport
🌐 Perfect for: Web-based deployments, microservices, remote access
🚀 Usage:
python mcp_server.py --transport http --port 9000🌐 URL:
http://127.0.0.1:9000/mcp/
3. SSE Transport (deprecated)
⚠️ Legacy support only - use HTTP transport for new projects
🚀 Usage:
python mcp_server.py --transport sse --port 9000
FastMCP CLI Usage
# Standard HTTP server
fastmcp run mcp_server.py -t streamable-http --port 9000 -l DEBUG
# With custom host
fastmcp run mcp_server.py -t streamable-http --host 0.0.0.0 --port 9000 -l DEBUG
# STDIO transport (for local clients)
fastmcp run mcp_server.py -t stdio
# Development mode with MCP Inspector
fastmcp dev mcp_server.py -t streamable-http --port 9000VS Code Development
Open in VS Code:
code .Use Debug Configurations:
Debug MCP Server (STDIO): Run with STDIO transportDebug MCP Server (HTTP): Run with HTTP transportDebug Tests: Run the test suite
Configuration
Environment Variables
Create a .env file based on .env.example:
# Server Settings
MCP_HOST=0.0.0.0
MCP_PORT=9000
MCP_DEBUG=false
MCP_SERVER_NAME=MHLABS MCP Server
# Authentication Settings
MCP_ENABLE_AUTH=true
AZURE_TENANT_ID=your-tenant-id-here
AZURE_CLIENT_ID=your-client-id-here
AZURE_JWKS_URI=https://login.microsoftonline.com/your-tenant-id/discovery/v2.0/keys
AZURE_ISSUER=https://sts.windows.net/your-tenant-id/
AZURE_AUDIENCE=api://your-client-idAuthentication
When MCP_ENABLE_AUTH=true, the server expects Azure AD Bearer tokens. Configure your Azure App Registration with the appropriate settings.
For development, set MCP_ENABLE_AUTH=false to disable authentication.
Adding New Services
Create Service Class:
from core.factory import MCPToolBase, Domain class MyService(MCPToolBase): def __init__(self): super().__init__(Domain.MY_DOMAIN) def register_tools(self, mcp): @mcp.tool(tags={self.domain.value}) async def my_tool(param: str) -> str: # Tool implementation pass @property def tool_count(self) -> int: return 1 # Number of toolsRegister in Server:
# In mcp_server.py (gets registered automatically from services/ directory) factory.register_service(MyService())Add Domain (if new):
# In core/factory.py class Domain(Enum): # ... existing domains MY_DOMAIN = "my_domain"
MCP Client Usage
Python Client
import asyncio
from fastmcp import Client
client = Client("http://localhost:9000/mcp")
async def main():
async with client:
tools = await client.list_tools()
# tools -> list[mcp.types.Tool]
# print(tools)
for tool in tools:
print(f"Tool: {tool.name}")
result = await client.call_tool("textprep.expand_contraction", {"input_text": "The must've SSN is 859-98-0987. The employee's phone number is 555-555-5555."})
print("Result:", result)
asyncio.run(main())Command Line Testing
# Test the server is running
curl http://localhost:9000/mcp/
# With FastMCP CLI for testing
fastmcp dev mcp_server.py -t streamable-http --port 9000Quick Test
Test STDIO Transport:
# Start server in STDIO mode
python mcp_server.py --debug --no-auth
# Test with client_example.py
python client_example.pyTest HTTP Transport:
# Start HTTP server
python mcp_server.py --transport http --port 9000 --debug --no-auth
# Test with FastMCP client
python -c "
from fastmcp import Client
import asyncio
async def test():
async with Client('http://localhost:9000/mcp') as client:
result = await client.call_tool("textprep.expand_contraction", {"input_text": "The must've SSN is 859-98-0987. The employee's phone number is 555-555-5555."})
print(result)
asyncio.run(test())
"Test with FastMCP CLI:
# Start with FastMCP CLI
fastmcp run mcp_server.py -t streamable-http --port 9000 -l DEBUG
# Server will be available at: http://127.0.0.1:9000/mcp/Troubleshooting
Common Issues
Import Errors: Make sure you're in the correct directory and dependencies are installed
Authentication Errors: Check your Azure AD configuration and tokens
Port Conflicts: Change the port in configuration if 9000 is already in use
Missing fastmcp: Install with
pip install fastmcp
Debug Mode
Enable debug mode for detailed logging:
python mcp_server.py --debug --no-authOr set in environment:
MCP_DEBUG=trueServer Arguments
usage: mcp_server.py [-h] [--transport {stdio,http,streamable-http,sse}]
[--host HOST] [--port PORT] [--debug] [--no-auth]
MHLABS MCP Server
options:
-h, --help show this help message and exit
--transport, -t Transport protocol (default: stdio)
--host HOST Host to bind to for HTTP transport (default: 127.0.0.1)
--port, -p PORT Port to bind to for HTTP transport (default: 9000)
--debug Enable debug mode
--no-auth Disable authentication📄 License
MIT License © 2025 MusaddiqueHussain Labs
🤝 Contributing
Follow the existing code structure and patterns
Add tests for new functionality
Update documentation for new features
Use the provided VS Code configurations for development
🧠 Learn More
MCP Protocol: https://modelcontextprotocol.io
FastMCP GitHub: https://github.com/fastmcp/fastmcp
LangGraph Integration Guide (coming soon)
💡 Tip
If you want to embed mhlabs-mcp-tools into a larger MCP-based orchestrator:
from fastmcp import StdioServerParameters
server_params = StdioServerParameters(
command="python",
args=["-m", "mhlabs_mcp_tools.server"],
//env={"MHLABS_MCP_CATEGORY": "textprep,nlp"}
)Developed with ❤️ by MusaddiqueHussain Labs
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