Coreshub MCP Server

Overview

The Coreshub MCP Server is a ready‑to‑deploy Model Context Protocol (MCP) implementation that bridges AI assistants with the Coreshub platform. It exposes a collection of reusable tools and prompts, allowing developers to extend the capabilities of large language models (LLMs) without modifying the core assistant code. By handling authentication, plugin discovery, and command routing internally, the server lets AI assistants invoke Coreshub services—such as file system access, data retrieval, or custom business logic—through simple, well‑defined MCP calls.

What problem does it solve?

Modern AI assistants often need to interact with external data stores, compute resources, or domain‑specific APIs. Without a standardized interface, each integration requires bespoke client code and increases maintenance overhead. The Coreshub MCP Server eliminates this friction by providing a unified, protocol‑level gateway that accepts tool and prompt requests from any MCP‑compliant client. Developers can focus on implementing business logic in isolated plugins, while the server handles authentication (via Coreshub access keys), context extraction, and response formatting.

Core value for developers

• Plug‑and‑play architecture – Tools and prompts live in a dedicated directory. Adding a new capability is as simple as dropping a Python module that subclasses or . The server automatically registers and exposes it to the assistant.
• Secure, context‑aware access – Environment variables (, , ) are used to authenticate requests against Coreshub’s API, ensuring that only authorized users can invoke sensitive operations.
• Rich metadata and schema support – Each tool defines a JSON schema for its arguments, allowing the assistant to validate inputs and generate precise prompts. Prompts can specify required or optional arguments, making interactions predictable.
• Cross‑platform deployment – The server can be launched from Cherry Studio, a simple command line, or any environment that supports communication. It works seamlessly on macOS and Windows with minimal configuration.

Key features

Real‑world use cases

• Enterprise file management – The tool lets assistants list or manipulate files stored in Coreshub’s EPFS, enabling knowledge workers to browse documents directly from chat. |
• Custom business logic – Developers can expose internal microservices (e.g., inventory lookup, workflow triggers) as MCP tools, allowing assistants to perform complex tasks on behalf of users. |
• Data‑driven decision making – Prompt plugins can query analytics APIs and return summaries or visualizations, empowering assistants to provide actionable insights. |
• Multi‑tenant SaaS – By tying tool access to the and fields, the server supports isolated environments for different customers. |

Integration into AI workflows

An MCP‑compliant assistant (e.g., Claude, Gemini) connects to the Coreshub server over a channel. When a user request matches a registered tool, the assistant sends a JSON payload describing the operation and its arguments. The server validates the schema, authenticates against Coreshub, executes the tool’s or coroutine, and streams back the result. Because the server operates independently of the assistant’s core logic, it can be swapped or upgraded without touching the LLM codebase.

Unique advantages

• Zero‑code client integration – Existing MCP clients need no changes; they treat the Coreshub server like any other tool provider. |
• Modular security – Credentials are injected via environment variables, keeping secrets out of source code. |
• Rapid iteration – Adding or updating a plugin does not require redeploying the assistant; only the server’s directory changes. |
• Community extensibility – The open‑source project encourages third‑party developers to publish plugins, expanding the ecosystem around Co