Overview of Searchcraft MCP Server

The Searchcraft MCP Server bridges the gap between AI assistants and a powerful, developer‑first vertical search engine. By exposing Searchcraft’s core capabilities through the Model Context Protocol, it allows tools such as Claude Desktop to perform sophisticated search‑related operations using plain‑English prompts. This eliminates the need for developers to write custom API clients or manage authentication manually, streamlining the workflow from data ingestion to real‑time search.

At its core, the server provides a comprehensive set of tools that mirror Searchcraft’s administrative API. Developers can create and delete indexes, adjust schemas, and manage documents—all via intuitive commands that an AI assistant interprets. In addition to index and document operations, the server supports federation management, enabling the aggregation of multiple indexes into a single search surface. Analytics tools expose document counts and statistics, giving instant visibility into the health of the cluster without leaving the AI interface.

Key capabilities include:

• Index Management: Create, delete, list, and patch indexes with full schema control.
• Document Management: Bulk ingestion, selective deletion by ID or field, and retrieval of individual documents.
• Federation Management: Build, update, and inspect federations that combine multiple indexes for unified search results.
• Analytics: Quickly fetch statistics such as document counts, index health, and federation metrics.

These tools are grouped into logical categories—engine API tools for direct cluster control, import tools for data ingestion, and app‑generation utilities that scaffold front‑end projects (e.g., Vite apps). A typical use case involves an AI assistant guiding a developer to set up a new e‑commerce search app: it creates an index from a JSON dataset, populates the index, generates an API read key, and then hands off control to a generated front‑end scaffold. This end‑to‑end flow demonstrates how the MCP server removes boilerplate and accelerates time‑to‑market.

Integration with AI workflows is seamless. An MCP client sends a natural‑language request, which the server translates into one of its pre‑defined tool calls. The response is then fed back to the assistant, allowing iterative refinement or error handling without manual intervention. This tight coupling empowers developers to prototype complex search solutions, perform rapid A/B testing, and maintain operational visibility—all through a single conversational interface.