CTERA Portal MCP Server

Overview

The mcp‑ctera-core server turns the CTERA Intelligent Data Services Platform into a first‑class AI assistant resource. By exposing CTERA’s file and folder management APIs through the Model Context Protocol (MCP), it allows Claude or other AI assistants to perform data‑centric operations—such as creating, moving, copying, and deleting files—directly from natural language commands or scripted workflows. This solves the common pain point of bridging conversational AI with enterprise data stores, eliminating the need for developers to write custom SDK wrappers or manage authentication flows manually.

At its core, the server authenticates against a CTERA Portal instance using environment‑driven credentials (host, user, password, SSL flag) and then maps each CTERA API endpoint to an MCP resource. The AI client can invoke these resources by name, passing structured arguments that mirror the CTERA API signature. The server handles request translation, error mapping, and response formatting so that the assistant can interpret results in plain language or JSON. This abstraction lets developers focus on business logic rather than low‑level API plumbing.

Key capabilities include:

• File and folder CRUD: Create, read, update, delete, move, and copy operations across any CTERA tenant.
• Search and metadata retrieval: Query files by name, size, or custom tags and retrieve detailed metadata for audit or reporting.
• Batch processing: Execute bulk operations through a single MCP call, reducing round‑trip latency.
• Extensibility hooks: The server exposes a lightweight plugin interface, allowing additional CTERA services (e.g., sharing, permissions) to be added without altering the core codebase.

Typical use cases are plentiful. A data‑science team can ask an assistant to “list all CSV files in the marketing folder,” and the server will return a structured list. An operations engineer might instruct Claude to “move all documents older than 90 days from the archive folder to long‑term storage,” and the assistant will orchestrate the move with a single command. In CI/CD pipelines, automated tests can query CTERA to verify that deployment artifacts have been correctly archived before proceeding.

Integration into AI workflows is straightforward. Once the MCP server is running—whether via standard I/O, SSE, or Docker—the AI client registers it as a resource provider. Subsequent conversations can reference CTERA actions by name, and the assistant’s natural language understanding layer will translate user intent into a precise MCP call. The server’s robust error handling ensures that failures (e.g., authentication errors, permission denials) are surfaced to the user with actionable messages.

What sets mcp‑ctera-core apart is its focus on security and ease of deployment. Credentials are supplied exclusively through environment variables, keeping secrets out of source control. The server’s Docker image bundles all dependencies, enabling rapid onboarding in containerized environments. By bridging conversational AI with a mature enterprise data platform, it empowers developers to build intelligent workflows that are both powerful and compliant with organizational security policies.