Infactory MCP Desktop Extension (DXT)

Infactory’s MCP Desktop Extension bridges the gap between raw data and conversational AI by turning documents, CSV files, and other structured assets into fully‑queryable APIs. For developers who rely on Claude or any MCP‑compatible assistant, the extension offers a turnkey workflow: upload data, generate semantic queries, expose those queries as HTTP endpoints, and interact with the data through natural language—all within a single desktop environment. This streamlines the process of turning static information into dynamic, AI‑ready resources without needing to write custom ingestion pipelines or server code.

The core value lies in the end‑to‑end automation of data preparation. Instead of manually cleaning, normalizing, and exposing datasets, Infactory’s DXT handles each step. Users can upload CSVs or other structured sources, which the service automatically parses and indexes for efficient retrieval. Once indexed, the extension can generate intelligent query templates or let developers craft custom queries from plain language prompts. These queries are then packaged as live API endpoints, ready to be called by any MCP client or external application. This eliminates the need for separate API development and deployment, allowing teams to focus on building higher‑level AI experiences.

Key capabilities include:

• Data Management – Seamless upload and organization of CSV files and other data sources within projects, with support for team collaboration.
• Query Generation – Automatic creation of query programs from natural language, reducing the barrier to accessing complex data structures.
• API Deployment – One‑click publishing of query programs as RESTful endpoints, enabling integration with web services, dashboards, or other tools.
• Chat Integration – Built‑in conversational interface that lets users explore data directly through Claude or other MCP clients.
• Project & Team Management – Structured project hierarchy and role‑based access control, facilitating collaboration across organizations.

In practice, Infactory’s MCP server shines in scenarios such as data‑driven research assistants, business analytics bots, or any application that requires up‑to‑date access to structured datasets. A data scientist can upload a CSV of experimental results, generate queries that compute statistical summaries, and expose those as APIs. A product manager can then query sales figures or user engagement metrics through a conversational interface, receiving instant insights without leaving their AI assistant. The ability to publish APIs on demand also supports rapid prototyping, allowing developers to expose new data sources as soon as they are ready.

Integration is straightforward for MCP‑aware workflows. Once the DXT is installed, developers configure the Infactory API key and optional base URL in the extension’s UI. The server is then added to any MCP client—Claude Desktop, Cursor, VS Code, or others—via a simple JSON snippet that points to the local Node package. From there, the MCP client can request resources, invoke tools, or sample responses, all backed by Infactory’s data processing engine. The result is a cohesive pipeline where data ingestion, query generation, and AI interaction coexist in a single, maintainable ecosystem.