Mhlabs Mcp Tools

io.github.MusaddiqueHussainLabs由 GitHub 账号 MusaddiqueHussainLabs 发布v3.0.0更新于 Nov 3, 2026

An MCP server that provides text preprocessing, NLP components, and document analysis

已验证STDIO仅桌面数据与分析AI 与机器学习

概览

AI 生成的概览

本地运行的 Python MCP 服务器,提供文本预处理、NLP 组件和文档分析工具,用于清洗与分析文本。

可以做什么
  • 清洗文本:大小写转换,去除数字、网址、标点、特殊字符、多余空白、HTML、表情符号与颜文字
  • 移除或脱敏邮箱、电话号码、社会安全号(SSN)和信用卡号等敏感模式
  • 规范化文本:Unicode 规范化、缩写展开、聊天用语与表情转文字、去停用词、去高频词与低频词
  • 分词:按词与按句切分,并进行词干化与词形还原
  • 运行 NLP 组件:词性标注、词形学、依存句法分析、命名实体识别与规范化
  • 通过一个组合预处理函数串联多个处理步骤
适用场景
适合在分析前需要清洗、脱敏或规范化原始文本的场景,也适合在本地完成分词、词性标注、词形还原和实体识别等基础 NLP 任务,而无需把文本发送到外部服务。
运行要求
以本地 Python 进程运行,从 PyPI 包 mhlabs-mcp-tools 安装,并通过 pip 安装依赖(含 fastmcp)。默认使用 STDIO 传输;HTTP/SSE 模式会占用端口。可选的 Azure AD 认证通过环境变量配置,如 MCP_ENABLE_AUTH、AZURE_TENANT_ID、AZURE_CLIENT_ID、AZURE_JWKS_URI、AZURE_ISSUER 和 AZURE_AUDIENCE。
安装前请注意
传入工具的文本由本地服务器处理;若改用 HTTP 传输并绑定非本机地址,端点可能被他人访问。认证是可选的,可通过 MCP_ENABLE_AUTH=false 或关闭认证参数禁用,共享部署时应保持开启。Azure AD 配置通过环境变量提供,应避免提交到版本库。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Mhlabs Mcp Tools,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

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.py

Available Services

Currently the package is organized into three primary modules:

1. NLP Components

Component TypeDescription
tokenizeText tokenization
posPart-of-Speech tagging
lemmaWord lemmatization
morphologyStudy of word forms
depDependency parsing
nerNamed Entity Recognition
normText normalization

2. Text Preprocessing

This module equips users with an extensive set of text preprocessing tools:

FunctionDescription
to_lowerConvert text to lowercase
to_upperConvert text to uppercase
remove_numberRemove numerical characters
remove_itemized_bullet_and_numberingEliminate itemized/bullet-point numbering
remove_urlRemove URLs from text
remove_punctuationRemove punctuation marks
remove_special_characterRemove special characters
keep_alpha_numericKeep only alphanumeric characters
remove_whitespaceRemove excess whitespace
normalize_unicodeNormalize Unicode characters
remove_stopwordEliminate common stopwords
remove_freqwordsRemove frequently occurring words
remove_rarewordsRemove rare words
remove_emailRemove email addresses
remove_phone_numberRemove phone numbers
remove_ssnRemove Social Security Numbers (SSN)
remove_credit_card_numberRemove credit card numbers
remove_emojiRemove emojis
remove_emoticonsRemove emoticons
convert_emoticons_to_wordsConvert emoticons to words
convert_emojis_to_wordsConvert emojis to words
remove_htmlRemove HTML tags
chat_words_conversionConvert chat language to standard English
expand_contractionExpand contractions (e.g., "can't" to "cannot")
tokenize_wordTokenize words
tokenize_sentenceTokenize sentences
stem_wordStem words
lemmatize_wordLemmatize words
preprocess_textCombine multiple preprocessing steps into one function

Quick Start

Development Setup

  1. Clone and Navigate:

    bash
    cd src/mhlabs_mcp_tools
  2. Install Dependencies:

    bash
    pip install -r requirements.txt
  3. Configure Environment:

    bash
    cp .env.example .env# Edit .env with your configuration
  4. Start the Server:

    bash
    # Default STDIO transport (for local MCP clients)python mcp_server.py
    # HTTP transport (for web-based clients)python mcp_server.py --transport http --port 9000orafter installed mhlabs-mcp-toolspython -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 disabledpython 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.py or python 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

bash
# Standard HTTP serverfastmcp run mcp_server.py -t streamable-http --port 9000 -l DEBUG
# With custom hostfastmcp 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 Inspectorfastmcp dev mcp_server.py -t streamable-http --port 9000

VS Code Development

  1. Open in VS Code:

    bash
    code .
  2. Use Debug Configurations:

    • Debug MCP Server (STDIO): Run with STDIO transport
    • Debug MCP Server (HTTP): Run with HTTP transport
    • Debug Tests: Run the test suite

Configuration

Environment Variables

Create a .env file based on .env.example:

env
# Server SettingsMCP_HOST=0.0.0.0MCP_PORT=9000MCP_DEBUG=falseMCP_SERVER_NAME=MHLABS MCP Server
# Authentication SettingsMCP_ENABLE_AUTH=trueAZURE_TENANT_ID=your-tenant-id-hereAZURE_CLIENT_ID=your-client-id-hereAZURE_JWKS_URI=https://login.microsoftonline.com/your-tenant-id/discovery/v2.0/keysAZURE_ISSUER=https://sts.windows.net/your-tenant-id/AZURE_AUDIENCE=api://your-client-id

Authentication

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

  1. Create Service Class:

    python
    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 tools
  2. Register in Server:

    python
    # In mcp_server.py (gets registered automatically from services/ directory)factory.register_service(MyService())
  3. Add Domain (if new):

    python
    # In core/factory.pyclass Domain(Enum):    # ... existing domains    MY_DOMAIN = "my_domain"

MCP Client Usage

Python Client

python
import asynciofrom 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

bash
# Test the server is runningcurl http://localhost:9000/mcp/
# With FastMCP CLI for testingfastmcp dev mcp_server.py -t streamable-http --port 9000

Quick Test

Test STDIO Transport:

bash
# Start server in STDIO modepython mcp_server.py --debug --no-auth
# Test with client_example.pypython client_example.py

Test HTTP Transport:

bash
# Start HTTP serverpython mcp_server.py --transport http --port 9000 --debug --no-auth
# Test with FastMCP clientpython -c "from fastmcp import Clientimport asyncioasync 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:

bash
# Start with FastMCP CLIfastmcp 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

  1. Import Errors: Make sure you're in the correct directory and dependencies are installed
  2. Authentication Errors: Check your Azure AD configuration and tokens
  3. Port Conflicts: Change the port in configuration if 9000 is already in use
  4. Missing fastmcp: Install with pip install fastmcp

Debug Mode

Enable debug mode for detailed logging:

bash
python mcp_server.py --debug --no-auth

Or set in environment:

env
MCP_DEBUG=true

Server Arguments

bash
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

  1. Follow the existing code structure and patterns
  2. Add tests for new functionality
  3. Update documentation for new features
  4. Use the provided VS Code configurations for development

🧠 Learn More


💡 Tip

If you want to embed mhlabs-mcp-tools into a larger MCP-based orchestrator:

python
from fastmcp import StdioServerParametersserver_params = StdioServerParameters(    command="python",    args=["-m", "mhlabs_mcp_tools.server"],    //env={"MHLABS_MCP_CATEGORY": "textprep,nlp"})

Developed with ❤️ by MusaddiqueHussain Labs

来源:README.md,提交 6c1b271

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v3.0.0最新Sep 16, 2026

评分

已满足适用检查项的 50%

该评分衡量此收录条目的完整程度,依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价,也不表示服务器安全或当前可用:可连通性是带日期的检查结果,评分也不包含安装量或用户评价。

  • 近 30 天内可连通25 分

    请保持端点能够响应 MCP 握手;SourceWeft 每 30 天探测一次。

  • 已声明认证方式10 分

    请在注册中心条目中声明凭据请求头,或保持端点能够响应探测,以便判定其认证方式。

  • 已列出工具15 分

    请让服务器在无凭据时响应 tools/list,或发布可在沙箱中无凭据启动的软件包。

  • 描述必需15 分
  • 图标10 分
  • 文档15 分
  • 许可证10 分