semvec/pypi-packages huggingface dataset:
konokenj.cdk-api-mcp-server 0.75.0
An MCP server provides AWS CDK API Reference
Requires Python: >=3.8

# CDK API MCP Server

[![PyPI - Version](https://img.shields.io/pypi/v/konokenj.cdk-api-mcp-server.svg)](https://pypi.org/project/konokenj.cdk-api-mcp-server)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/konokenj.cdk-api-mcp-server.svg)](https://pypi.org/project/konokenj.cdk-api-mcp-server)

<!-- DEP-VERSIONS-START -->
[![aws-cdk](https://img.shields.io/badge/aws%20cdk-v2.238.0-blue.svg)](https://github.com/konokenj/cdk-api-mcp-server/blob/main/current-versions/aws-cdk.txt)
<!-- DEP-VERSIONS-END -->

---

Provide AWS CDK API references and integration test code for sample. Can be used in offline because all documents are included in the released python artifact.

## Usage

Add to your mcp.json:

```json
{
  "mcpServers": {
    "konokenj.cdk-api-mcp-server": {
      "command": "uvx",
      "args": ["konokenj.cdk-api-mcp-server@latest"]
    }
  }
}
```

## MCP Server Capabilities

### Resource: CDK API packages

Registered as static resources. To get available modules under the package, call `list_resources()` as MCP client.

- `cdk-api-docs://constructs/@aws-cdk` ... Alpha modules published in `@aws-cdk` namespace
- `cdk-api-docs://constructs/aws-cdk-lib` ... Stable modules in `aws-cdk-lib` package

### Resource Template: List modules in package

To get available documents under the module, call `read_resource(uri)` as MCP client.

- `cdk-api-docs://constructs/@aws-cdk/{module}`
- `cdk-api-docs://constructs/aws-cdk-lib/{module}`

### Resource Template: Read file contents

To read a document, call `read_resource(uri)` as MCP client.

- `cdk-api-docs://constructs/@aws-cdk/{module}/{file}`
- `cdk-api-docs://constructs/aws-cdk-lib/{module}/{file}`

## License

Distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
openenv-cli 0.0.1
A professional-grade AI utility for automated data synchronization and backend management.
Requires Python: >=3.8
Keywords: automation, api-client, sync, tooling



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md
iceprod 3.2.21
IceCube dataset management system
Requires Python: <3.15,>=3.12
Keywords: WIPAC, batch, workload

IceProd
=======

.. image:: https://zenodo.org/badge/58235078.svg
   :target: https://zenodo.org/badge/latestdoi/58235078

IceProd is a Python framework for distributed management of batch jobs.
It runs as a layer on top of other batch middleware, such as HTCondor,
and can pool together resources from different batch systems.
The primary purpose is to coordinate and administer many large sets of
jobs at once, keeping a history of the entire job lifecycle.

See also: Aartsen, Mark G., et al. "The IceProd framework: Distributed data processing for the IceCube neutrino observatory." Journal of parallel and distributed computing 75 (2015): 198-211.

**Note:**

For IceCube users with CVMFS access, IceProd is already installed.
To load the environment execute::

    /cvmfs/icecube.wisc.edu/iceprod/latest/env-shell.sh

or::

    eval `/cvmfs/icecube.wisc.edu/iceprod/latest/setup.sh`

depending on whether you want to get a new shell or load the variables
into the current shell.

Installation
------------

**Platforms**:

IceProd should run on any Unix-like platform, although only
Linux has been extensively tested and can be recommented for production
deployment.

**Prerequisites**:

Listed here are any packages outside pip:

* Python 3.7+
* MongoDB 3.6+    (for the REST API)
* nginx           (for ssl offloading and better security)
* globus          (for data transfer)

**Installation**:

From the latest release:

Get the tarball link from https://github.com/WIPACrepo/iceprod/releases/latest

Then install like::

    pip install https://github.com/WIPACrepo/iceprod/archive/v2.0.0.tar.gz

**Installing from master**:

If you must install the dev version from master, do::

    pip install --upgrade git+git://github.com/WIPACrepo/iceprod.git#egg=iceprod