Pocketbase MCP Server

PocketBase MCP Server

The PocketBase MCP Server bridges the gap between AI assistants and a PocketBase backend by exposing a lightweight, TypeScript‑based MCP interface. It solves the common pain point of retrieving structured data from PocketBase collections in a format that Claude or other AI tools can consume without writing custom connectors. By running as an MCP server, it automatically registers a tool that lists all collections in the configured PocketBase instance, returning a clean JSON representation that can be passed directly into prompt templates or used to drive dynamic workflows.

Developers working with AI assistants often need real‑time access to database schema or metadata. The PocketBase MCP Server provides a single, well‑defined tool—. When invoked, the server authenticates to PocketBase using admin credentials supplied via command‑line flags or environment variables, queries the collections endpoint, and serializes the result into JSON. This eliminates boilerplate code for authentication, error handling, and data formatting, allowing developers to focus on higher‑level logic such as generating reports or building interactive dashboards powered by AI.

Key features of the server include:

• Secure configuration: Admin credentials are passed through environment variables or CLI arguments, keeping secrets out of source code.
• Minimal footprint: Written in TypeScript with a small dependency set, the server starts quickly and consumes little memory.
• Standard MCP compliance: By adhering to the Model Context Protocol, it can be plugged into any MCP‑compatible client, including Claude Desktop, without modification.
• Extensibility: The architecture is designed for easy addition of new tools, such as querying records or performing CRUD operations, by following the same pattern.

Typical use cases span a range of scenarios. A data analyst might ask an AI assistant to list all collections before crafting a query, while a product manager could use the tool to verify schema changes during deployment. In CI/CD pipelines, the server can be invoked by automated scripts to generate documentation of database structure or to validate that new collections meet naming conventions.

Integration into AI workflows is straightforward: once the MCP server is registered in a client’s configuration, any prompt can call as an external tool. The AI receives the JSON list, can parse it to display a table, or use it as input for subsequent tool calls. This seamless interaction turns raw database metadata into actionable insights, reducing turnaround time and minimizing the risk of manual errors.

Overall, the PocketBase MCP Server offers a focused, secure, and protocol‑compliant solution for developers who need quick access to PocketBase collection information within AI‑driven applications. Its simplicity, coupled with the flexibility of MCP, makes it an attractive addition to any modern development stack that leverages AI assistants for data exploration and automation.