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iarsingh

Multi-MCP AI Assistant

by iarsingh
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Python
Remote

Multi-MCP AI Assistant

Project guide

Project architecture · Interview questions and answers

Use the architecture document for the component diagram, implementation boundaries, and verification entry points. The interview guide includes source-backed answers and project walkthroughs.

Implementation map

Component

Responsibility

src/multimcp/main.py

HTTP handlers: GET /healthz, GET /tools, POST /call

src/multimcp/mcp.py

Functions: list_tools, call

requirements.txt

Implementation or supporting configuration

src/multimcp/__init__.py

Implementation or supporting configuration

tests/test_mcp.py

Executable checks and regression examples

.github/workflows/ci.yml

GitHub Actions job definitions

README.md

Project explanations or operating notes

Local setup and verification

From the repository root (the commands follow the checked-in manifests):

python3 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txt
python -m pytest -q

To serve the FastAPI application locally, install the server separately if it is not already available:

python -m pip install uvicorn
PYTHONPATH=src python -m uvicorn multimcp.main:app --reload

Level: 10 — MCP & Tool-Using Agents

Skills: Python, several MCP servers in one list

Tools from github, k8s, and db namespaces. Apply refused.

pip install -r requirements.txt
pytest -q

This is a local laptop proof. It does not call a hosted model and it does not apply production changes.

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityMaintained
ResponsivenessNo issues

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