Browse all content tagged with Python
py-mcp-line is a Python-based MCP server that enables AI assistants to access and interact with LINE Bot messages. It exposes resources, supports advanced filtering, and provides tools for conversational data analysis, chatbot development, and message archiving.
The AWS Resources MCP Server lets AI assistants manage and query AWS resources conversationally using Python and boto3. Integrate powerful AWS automation and management into your FlowHunt workflows with secure, permission-based operations.
Integrate AI assistants with the Terraform Cloud API using the Terraform Cloud MCP Server. Manage infrastructure through natural language, automate workspace and project tasks, and streamline infrastructure-as-code workflows, all from your favorite AI platforms.
Video Still Capture MCP is a Python-based server that provides AI assistants with real-time webcam and video source access through OpenCV, enabling image capture, camera management, and basic image processing via standardized Model Context Protocol interfaces.
The MCP-Server-Creator is a meta-server that enables rapid creation and configuration of new Model Context Protocol (MCP) servers. With dynamic code generation, tool building, and resource management, it streamlines the development of custom AI connectivity and integration servers, empowering technical teams to automate workflows and accelerate deployment.
The MetaTrader MCP Server connects AI Large Language Models to MetaTrader 5, enabling automated trading, portfolio management, and intelligent market analysis directly from AI-driven workflows.
The MCP Code Executor MCP Server enables FlowHunt and other LLM-driven tools to securely execute Python code in isolated environments, manage dependencies, and dynamically configure code execution contexts. It is ideal for automated code evaluation, reproducible data science workflows, and dynamic environment setup inside FlowHunt flows.
The QGIS MCP Server bridges QGIS Desktop with LLMs for AI-driven automation—enabling project, layer, and algorithm control, as well as Python code execution directly from conversational interfaces.
The pydanticpydantic-aimcp-run-python MCP Server bridges AI assistants with secure, controlled Python code execution environments. It enables dynamic Python scripting, automation, and parallel function execution within FlowHunt and compatible platforms.
Discover a scalable Python solution for invoice data extraction using AI-based OCR. Learn how to convert PDFs, upload images to FlowHunt’s API, and retrieve structured data efficiently in CSV format, streamlining your document processing workflows.
Anaconda is a comprehensive, open-source distribution of Python and R, designed to simplify package management and deployment for scientific computing, data science, and machine learning. Developed by Anaconda, Inc., it offers a robust platform with tools for data scientists, developers, and IT teams.
Chainer is an open-source deep learning framework offering a flexible, intuitive, and high-performance platform for neural networks, featuring dynamic define-by-run graphs, GPU acceleration, and broad architecture support. Developed by Preferred Networks with major tech contributions, it’s ideal for research, prototyping, and distributed training, but is now in maintenance mode.
A confusion matrix is a machine learning tool for evaluating the performance of classification models, detailing true/false positives and negatives to provide insights beyond accuracy, especially useful in imbalanced datasets.
Dash is an open-source Python framework by Plotly for building interactive data visualization applications and dashboards, combining Flask, React.js, and Plotly.js for seamless analytics and business intelligence solutions.
Gensim is a popular open-source Python library for natural language processing (NLP), specializing in unsupervised topic modeling, document indexing, and similarity retrieval. Efficiently handling large datasets, it supports semantic analysis and is widely used in research and industry for text mining, classification, and chatbots.
Google Colaboratory (Google Colab) is a cloud-based Jupyter notebook platform by Google, enabling users to write and execute Python code in the browser with free access to GPUs/TPUs, ideal for machine learning and data science.
Jupyter Notebook is an open-source web application enabling users to create and share documents with live code, equations, visualizations, and narrative text. Widely used in data science, machine learning, education, and research, it supports over 40 programming languages and seamless integration with AI tools.
Keras is a powerful and user-friendly open-source high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It enables fast experimentation and supports both production and research use cases with modularity and simplicity.
Natural Language Toolkit (NLTK) is a comprehensive suite of Python libraries and programs for symbolic and statistical natural language processing (NLP). Widely used in academia and industry, it offers tools for tokenization, stemming, lemmatization, POS tagging, and more.
NumPy is an open-source Python library crucial for numerical computing, providing efficient array operations and mathematical functions. It underpins scientific computing, data science, and machine learning workflows by enabling fast, large-scale data processing.
Pandas is an open-source data manipulation and analysis library for Python, renowned for its versatility, robust data structures, and ease of use in handling complex datasets. It is a cornerstone for data analysts and data scientists, supporting efficient data cleaning, transformation, and analysis.
Plotly is an advanced open-source graphing library for creating interactive, publication-quality graphs online. Compatible with Python, R, and JavaScript, Plotly empowers users to deliver complex data visualizations and supports a wide range of chart types, interactivity, and web app integration.
Scikit-learn is a powerful open-source machine learning library for Python, providing simple and efficient tools for predictive data analysis. Widely used by data scientists and machine learning practitioners, it offers a broad range of algorithms for classification, regression, clustering, and more, with seamless integration into the Python ecosystem.
SciPy is a robust open-source Python library for scientific and technical computing. Building on NumPy, it offers advanced mathematical algorithms, optimization, integration, data manipulation, visualization, and interoperability with libraries like Matplotlib and Pandas, making it essential for scientific computing and data analysis.
spaCy is a robust open-source Python library for advanced Natural Language Processing (NLP), known for its speed, efficiency, and production-ready features like tokenization, POS tagging, and named entity recognition.
