PrivateGPT MCP Server

The Mcp Server For Mas Developments is a purpose‑built Model Context Protocol (MCP) host that bridges AI assistants with secure, fine‑tuned data sources and tooling. In many modern deployments, an LLM needs to access protected APIs, internal knowledge bases, or custom data pipelines. Rather than exposing those resources directly to the model—risking accidental leakage or misuse—the MCP server acts as a gatekeeper, translating high‑level agent requests into concrete HTTP calls while enforcing authentication, authorization, and logging.

At its core, the server implements a rich set of RESTful endpoints that mirror typical chatbot interactions: chat creation, message sending, group management, and source handling. Each endpoint is guarded by TLS, password encryption, and token‑based authorization. The design allows developers to plug in new data sources (e.g., internal databases, document stores) by extending the source management module without touching the agent code. This modularity means a single MCP instance can serve multiple agents, each with its own permission set and configuration profile.

Key capabilities include:

• Fine‑grained access control – Users or agents are assigned to groups that limit which sources and actions they can invoke, reducing the attack surface.
• Secure credential handling – Passwords are encrypted on the client side and decrypted only on the server, ensuring that secrets never travel in plain text.
• Comprehensive logging – Every request is recorded with IP addresses, timestamps, and action details, facilitating audit trails and debugging.
• Flexible configuration – Through a YAML/JSON config file, developers can toggle features such as login/logout flows, chat persistence, or OpenAI‑compatible API endpoints.

Real‑world scenarios that benefit from this server include corporate knowledge bases where employees query confidential documents via an AI assistant, or research teams that need to pull from internal experiment databases while keeping the LLM sandboxed. In a typical workflow, an agent receives user input, consults the MCP to retrieve or update data, and then passes the result back to the LLM for natural‑language rendering. The server’s design ensures that data never leaves the controlled environment unless explicitly authorized.

What sets this MCP apart is its emphasis on security and operational transparency. By combining TLS, encrypted credentials, certificate‑based access control, and detailed logging, it offers a hardened platform that satisfies compliance requirements while still delivering the flexibility developers expect from modern AI toolchains.