semvec/pypi-packages huggingface dataset:
iflow-mcp_assemblyai-community-mcp-progression 0.1.0
A Model Context Protocol server for Google Docs integration
Requires Python: >=3.11

# Model Context Protocol

Repository for the article [Model Context Protocol (MCP) - What it is, how it works, and why it matters](https://www.assemblyai.com/blog/what-is-model-context-protocol-mcp)

Learn about the progression of AI agents:

1. Simple "bare" LLMs
2. LLMs + Tools
3. LLMs + Tools + APIs
4. LLMs + MCP
jmktools 0.2b1
JMK Engineering Python Toolboox
Requires Python: >=3.13

# JMK Engineering Python Tools

or JMKTools for short.

I don't know if this should be called a library or a set of modules. What it is, is a set of functions that replace calculations that I do on a weekly basis.

The library is broken into sections, with more being added later, including circuits and pv.

Some of these libraries rely on publicly accessible information, including manufacturer data sheets. The libraries that rely on copyrighted information you need to make sure that you have a copy of the standard, code, etc before initializing the software.

Use the example python notebook to get started.

## What we're missing

Tests, I haven't written any real tests for this set of libraries, yet. That will be coming soon, probably the next time that we have a slow time.


# Limitations

Currently the tools use the CEC, in most cases this will match the NEC, but something we are working on is updating that so we can select from which code version should be used for the calculation.

Another limitation is the use of SQL databases. This limits its functionality as a module, but my ability with pandas dataframe lookups is very weak. If anyone wants to make the changes and submit a pull request, we can develop that method on a different branch and test it to the main in the near future.

# Future Features

There are a lot of different features that I am planning, including building a web app that will allow this tool to be easily used from a browser, but before I get to that the largest feature that I need to figure out is how to install this as a module so that it can be called from any script that you want.
databricks-labs-lakebridge 0.12.1
Fast and predictable migrations to Databricks Lakehouse Platform. This tool is designed to help you migrate your data and workloads to the Databricks Lakehouse Platform in a fast, predictable, and reliable way. It provides a set of tools and utilities to help you reconcile your data and workloads, assess your current state, and plan your migration.
Requires Python: <3.14.0,>=3.10.1
Keywords: Databricks

![Databricks Labs Lakebridge](/docs/lakebridge/static/img/lakebridge-lockup-white-background.svg)
===============
by [Databricks Labs](https://www.databricks.com/learn/labs)

[![build](https://github.com/databrickslabs/lakebridge/actions/workflows/push.yml/badge.svg)](https://github.com/databrickslabs/remorph/actions/workflows/push.yml)
![PyPI - Downloads](https://img.shields.io/pypi/dm/databricks-labs-remorph?cacheSeconds=3600)

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Documentation is available at: https://databrickslabs.github.io/lakebridge/

Contribution
------------

Please see the contribution guidance [here](docs/lakebridge/docs/dev/contributing.md) on how to contribute to the project (build, test, and submit a PR).

Project Support
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Please note that this project is provided for your exploration only and is not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS, and we do not make any guarantees. Please do not submit a support ticket relating to any issues arising from the use of this project.

Any issues discovered through the use of this project should be filed as [GitHub Issues](https://github.com/databrickslabs/lakebridge/issues/) on this repository. They will be reviewed as time permits, but no formal SLAs for support exist.