What is Aap Mcp Server Python

aap-mcp-server-python is a Python-based server implementation that provides a FastAPI RESTful interface for managing and interacting with MCP (Multi-Channel Processing) functionalities.

Use cases

Use cases include building APIs for data processing, integrating with other services, and developing applications that require real-time data handling and interaction.

How to use

To use aap-mcp-server-python, set up a virtual environment, activate it, install the necessary packages, and run the server using Uvicorn. For example, in Example-1, you can run ‘uvicorn mcp_server:app --reload’ after installation.

Key features

Key features include a simple setup process, support for FastAPI, RESTful API endpoints, and the ability to handle multipart requests and YAML configuration files.

Where to use

aap-mcp-server-python can be used in web applications, microservices architectures, and any project that requires a RESTful interface for MCP functionalities.

For all projects here:

python -m venv .venv
source .venv/bin/activate
pip install fastapi uvicorn python-multipart pyyaml mcp json_rpc

ollama pull phi4:14b
ollama pull qwen3:0.6b
ollama serve # servers all available models

ansible-iq (Future Public Repo)

See readme inside this directory. Note assumes that ollama is running as a service and has qwen3:0.6b installed

Example-1 is a single file python program with a config.yaml specifying important files.

This example only implements a fastapi RESTful interface

cd example-1
uvicorn mcp_server:app --reload

Example-2 also implement FastMCP but maintains the FASTAPI endpoints as well.

cd example-2
PYTHONPATH=. python main.py --http

Example-3 mcp server from scratch - runs with mcp_parser

make sure ollama is running qwen3:0.6b

$ ps -aux | grep -i ollama
ollama      1933  0.0  0.0 2223700 29884 ?       Ssl  11:14   0:00 /usr/local/bin/ollama serve
arestlel  389077  0.0  0.0 230348  2196 pts/6    S+   15:27   0:00 grep --color=auto -i ollama
$ sudo ss -ltnp | grep ollama
[sudo] password for arestlel: 
LISTEN 0      4096       127.0.0.1:11434      0.0.0.0:*    users:(("ollama",pid=1933,fd=3))  

curl http://127.0.0.1:11434/api/generate \
  -d '{
    "model": "qwen3:0.6b",
    "prompt": "What model is running?",
    "stream": false
  }'
{"model":"qwen3:0.6b","created_at":"2025-05-21T19:31:32.28532585Z","response":"\u003cthink\u003e\nOkay, ...

If that is all running correctly then:

cd example-3
uvicorn main:app --host 127.0.0.1 --port 8000 --reload

# in another terminal
cd mcp_parser

# send a quesion to qwen3 llm model
curl -X POST http://127.0.0.1:8000/mcp/qwen3   -H "Content-Type: application/json"   -d '{"prompt": "Analyze this samba config", "tool": null}'

mcp_parser

Copy your tarball sosreports into the tar folder.

cd mcp_parser...
python main.py ../tar/sosreport-VA807527-04112973-2025-04-11-frwjgxp.tar.xz > sos.json # create a parsed json file
uvicorn mcp_server:app --reload

 python sos_chat_tui.py ../tar/sosreport-VA807527-04112973-2025-04-11-frwjgxp.tar.xz 

ansible-iq

Meant to be a public repo - but ran into trouble with the llm being “too clever” and ignoring the input from the mcp server.

References

• Claude and MCP – GitHub project exploring integration of Claude with an MCP server for observability workflows.

REDHAT MCP