PocketBase MCP Server

Overview

The PocketBase MCP Server bridges the gap between conversational AI assistants and a PocketBase backend, enabling seamless data manipulation directly from natural language interactions. By exposing a rich set of record‑management tools—fetch, list, create, update, and delete—developers can empower AI agents to query, modify, and orchestrate data stored in PocketBase collections without writing custom API wrappers. This eliminates the need for manual HTTP requests or boilerplate code, allowing rapid prototyping of data‑centric workflows.

The server is built around a straightforward authentication model that relies on a PocketBase admin token. Once configured, the MCP server exposes intuitive tools for each common CRUD operation. For example, retrieves a single entry by ID, while supports pagination, filtering, sorting, and relation expansion. These capabilities are defined through clear JSON schemas that the AI client can introspect, ensuring type‑safe interactions and reducing runtime errors.

Key features include:

• Unified Record Interface: A single toolset for all CRUD operations across any PocketBase collection, eliminating the need to write separate handlers.
• Advanced Query Support: Pagination, filtering, sorting, and relation expansion are all supported out of the box, allowing complex data retrieval in a single call.
• Secure Admin Access: Uses PocketBase’s admin API token for authentication, ensuring that only authorized agents can modify data.
• Schema‑Driven Inputs: Each tool’s input schema is exposed to the AI client, enabling automatic prompt generation and validation.
• Extensible Architecture: The server’s design allows additional tools (e.g., file management) to be added with minimal effort.

Typical use cases span from content management systems, where an AI assistant can draft, retrieve, and update articles stored in PocketBase, to data‑driven decision support tools that pull real‑time metrics and update dashboards. In a multi‑agent environment, one agent could gather data via , while another processes the results and writes back updates using or . The MCP server’s tight integration with AI workflows means developers can focus on business logic rather than plumbing, leading to faster delivery and more interactive applications.