Shallow Research Code Assistant

Shallow Research Code Assistant – A Multi‑Agent MCP Server for Rapid AI‑Powered Coding

The Shallow Research MCP Hub addresses a common pain point in modern AI development: bridging the gap between natural language queries and reliable, executable code. Developers often need to prototype algorithms, validate data pipelines, or explore new libraries quickly, yet the process of translating a user’s intent into working Python code and ensuring it runs correctly can be tedious. This server automates that workflow by combining web‑search‑driven research with on‑demand code generation and sandboxed execution, all orchestrated through Gradio’s Model Context Protocol.

At its core, the server runs a multi‑agent architecture. A research agent first expands the user’s request, performing targeted web searches and summarizing findings with proper citations. This “shallow” research phase supplies the context that informs the subsequent code‑generation step, allowing the assistant to reference up‑to‑date libraries and best practices. A dedicated coding agent then produces Python code tailored to the task, including data‑processing snippets, model training loops, or API calls. To guarantee correctness, the generated code is automatically executed inside a lightweight Modal sandbox that contains only essential packages such as pandas, numpy, requests, and scikit‑learn. If additional dependencies are required, the sandbox installs them on demand before running the script.

The result is a single‑click pipeline: a user submits a question, receives a concise summary of relevant research, and obtains verified Python code—all without leaving the MCP client. The server’s integration with popular LLM providers (Nebius, OpenAI, Anthropic, Hugging Face) lets developers choose the model that best fits their latency or cost constraints. Because each step is encapsulated in a separate agent, developers can easily extend the system—adding new research modules, custom execution environments, or domain‑specific agents—while preserving the overall workflow.

Typical use cases include rapid prototyping of data‑science workflows, generating boilerplate code for new projects, or creating educational examples that demonstrate how to use a particular library. In enterprise settings, the sandboxed execution ensures that code runs in a controlled environment, mitigating security risks. For researchers and hobbyists alike, the Shallow Research MCP Hub provides a powerful, low‑friction tool that transforms natural language queries into trustworthy, runnable Python scripts.