Content server for AI agents via MCP protocol

Stash-MCP: Content Server for AI Agents via MCP Protocol

Stash-MCP is a file-backed content server designed to expose documents to AI agents using the Model Context Protocol (MCP). This tool simplifies the process of providing persistent, accessible data stores for your AI agents, allowing them to retrieve and utilize information efficiently. Built with developers in mind, Stash-MCP integrates seamlessly into existing workflows and provides a robust foundation for agent-based applications requiring structured data access.

What it Does

Stash-MCP acts as a bridge between your file system and AI agents communicating via the MCP protocol. It indexes files within a specified directory and makes them available as context for agents. This means your AI agents can query and retrieve specific documents or pieces of information directly from your local storage, enabling them to perform tasks that require access to a knowledge base or dataset. The server handles the MCP communication, abstracting away the complexities of data retrieval for the agent.

Key Features

• File-Backed Storage: Utilizes your local file system as the primary data source, offering flexibility and control over your content.
• MCP Protocol Compliance: Exposes content via the Model Context Protocol, ensuring compatibility with MCP-enabled AI agents.
• Developer-Focused: Designed for integration into development environments, providing a straightforward way to provision agent context.
• Source Available: Open-source on GitHub, allowing for inspection, modification, and community contributions.

Who it's For

Stash-MCP is an essential tool for AI developers building agents that require access to external data. This includes:

• Developers creating AI agents for knowledge retrieval and question answering.
• Teams building complex AI systems that need to manage and serve large datasets to multiple agents.
• Researchers experimenting with agent architectures and data provisioning strategies.
• Anyone looking for a simple, file-based solution to provide context to their MCP-compatible AI agents.