semvec/pypi-packages huggingface dataset:
orange-raccoonai-mcp-server 0.0.1
A MCP server for Raccoon AI LAM API
Requires Python: >=3.10

# Raccoon AI MCP Server

[![smithery badge](https://smithery.ai/badge/@raccoonaihq/raccoonai-mcp-server)](https://smithery.ai/server/@raccoonaihq/raccoonai-mcp-server)
[![MCP Spec](https://img.shields.io/badge/mcp-compatible-green)](https://modelcontextprotocol.io)

Raccoon AI's Model Context Protocol (MCP) server that enables leveraging the LAM API for web browsing, data extraction, and complex web tasks automation.

## What can you do with this?

- Search and browse websites
- Fill out forms and navigate UI elements
- Extract structured data based on defined schemas
- Handle multistep processes across websites

## Prerequisites

Before using the Raccoon LAM MCP server, you'll need:

- Python 3.8 or higher
- [Claude Desktop](https://claude.ai/download) or another MCP-compatible client
- Raccoon AI Secret Key and your Raccoon Passcode

## Installation

### Using Smithery

```bash
npx -y @smithery/cli@latest install @raccoonaihq/raccoonai-mcp-server --client claude
```


### From source

```bash
git clone https://github.com/raccoonaihq/raccoonai-mcp-server.git
```
```bash
cd raccoonai-mcp-server
```
```bash
uv pip install -e .
```

#### To configure in Claude Desktop

```bash
mcp install src/raccoonai_mcp_server/server.py -v RACCOON_SECRET_KEY=<RACCOON_SECRET_KEY> -v RACCOON_PASSCODE=<RACCOON_PASSCODE>
```

Replace `<RACCOON_SECRET_KEY>` and `<RACCOON_PASSCODE>` with your actual creds. You can find them [here](https://platform.flyingraccoon.tech).

## Examples

Here are some example prompts that can be used with Claude to perform a variety of web tasks:

1. Can you extract product information from Amazon.com for the top-rated gaming keyboards?
2. Find and summarize the latest news articles about renewable energy technologies.
3. Find the 3 latest iPhone models and extract the details in a schema.
4. Do a deepsearch and generate a detailed report on Small Language Models.

## Documentation

For more information, refer to:
- [Raccoon LAM API Documentation](https://docs.flyingraccoon.tech/reference/lam/run)
- [Model Context Protocol Documentation](https://modelcontextprotocol.io)
sofaman 0.1.7
Model your architecture in an easy to understand language inspired by Yaml, and generate XMI (UML2) and PlantUML outputs.
Requires Python: >=3.12

# SofaMan

![Sofaman build status on GitHub Actions](https://github.com/ivellapillil/sofaman/actions/workflows/push-build-test.yaml/badge.svg?branch=main&event=push)

SofaMan is an easy to use software architecture modeling language inspired by YAML. 
It is designed to be easy to read and write, and to be easily converted to other modeling language formats.
At present there are two languages supported, XMI (UML2) and PlantUML.

The project uses [Pixi](https://pixi.sh/latest/) for project management.

## Example

The following shows an example model using the SofaMan modeling language.

```
#
# Sample sofa model.
#

package Retail.CRM:
    visibility: public

stereotype Regulatory: [GDPR]
stereotype Security: [PIDRelevant]

actor Client
actor RelationshipManager

component RESTService:
    ports: [443]

component CustomerDB:
    name: Customer Database
    description:|
        Holds customer data
    stereotypes: [Regulatory.GDPR]
    package: Retail.CRM
    ports: [443, REST]

component CustomerSearch(RESTService):
    package: Retail
    ports: [443]

relation CustomerSearch@443 flow CustomerDB@REST:
    name: Execute search query
    description: |
        Search for customers by name or address
    stereotypes: [Regulatory.GDPR]


```

The above model generates the following PUML.

```puml
@startuml sample
allowmixing

actor Client 

actor RelationshipManager 

component RESTService  {
    port 443
}

package Retail.CRM { 
component CustomerDB <<GDPR>> {
    port 443
    port REST
}
 
}
package Retail { 
component CustomerSearch  {
    port 443
}
 
}
443 ..> REST

@enduml

```

All the supported modeling syntax can be found in `tests/test_cases/full_all.sofa`

## Usage

### Run

```
pixi run python sofaman/sofamangen.py --help
```

### Run tests

```
pixi run test
```

### Test coverage
```
pixi run coverage
```

### Documentation
```
pixi run doc
```

The latest documentation can be found at: https://ruhati.net/sofaman/

## Using without Pixi

```
pip install sofaman
python -m sofaman.sofamangen --help
```

## Generate UML2 XMI

```
python -m sofaman.sofamangen generate a_model.sofa a_model.xmi
```

If you already have a XMI file and want to ensure updates to it are done using the same IDs (so as to preserve the diagram and other elements)

1. Generate the ID file
    ```
    python -m sofaman.sofamangen export a_model.xmi a_model_id.json
    ```

2. You can use the id file while re-generating the XMI
    ```
    python -m sofaman.sofamangen generate a_model.sofa a_model.xmi --ids_file 
    ```