Gridly MCP Server

Gridly MCP Server

The Gridly MCP Server bridges Claude and other MCP‑compatible assistants with the Gridly API, providing a unified interface for managing projects, databases, grids, and collaborative views. By exposing Gridly’s REST endpoints as MCP tools, it eliminates the need for developers to write custom HTTP clients or handle authentication logic manually. Instead, AI assistants can invoke high‑level operations—such as listing all databases in a project or adding records to a view—directly from the conversation context, streamlining data workflows and accelerating prototype development.

At its core, the server translates MCP tool calls into authenticated requests to Gridly’s API. Each tool maps cleanly onto a specific resource: Project tools let users enumerate or fetch projects; Database tools handle database enumeration and retrieval; Grid tools support CRUD operations on grids within a database; View, Column, and Dependency tools manage collaborative views, schema columns, and inter‑grid relationships; finally, Record tools allow record manipulation within views. Because the server runs as a local MCP service, it can be started with a single command and configured via environment variables or the client’s configuration file, making it easy to integrate into existing development environments.

Key capabilities include:

• Auth‑less operation for assistants: The server injects the Gridly API key into each request, so the assistant never needs to expose credentials.
• Fine‑grained resource control: Developers can specify exactly which grids, views, or records to manipulate, enabling precise data handling in AI‑driven applications.
• Collaborative workflow support: Tools for creating and updating views allow multiple assistants or users to share and edit data structures in real time.
• Auditability: Record history retrieval gives assistants visibility into changes, supporting traceable decision making.

Typical use cases span from rapid data exploration—where an assistant lists all databases and suggests the most relevant one—to automated report generation, where records are fetched, transformed, and inserted into new grids on the fly. In educational settings, students can interact with a live database through natural language commands, while data scientists can prototype ETL pipelines by chaining Gridly tools within an AI workflow. The server’s tight integration with MCP clients means these interactions feel native to the assistant, requiring no additional SDKs or boilerplate code.