Agentset MCP

The Agentset MCP server is a specialized bridge that connects Claude and other AI assistants to the Agentset platform—a robust, open‑source solution for Retrieval‑Augmented Generation (RAG). By exposing a standard MCP interface, the server allows developers to inject dynamic, document‑centric knowledge bases into conversational agents without having to build custom integrations from scratch. This solves the common pain point of marrying large language models with external, structured data: developers can now treat any Agentset namespace as a first‑class tool, automatically fetching relevant documents, summarizing them, or executing RAG pipelines as part of the assistant’s reasoning process.

At its core, the server provides a single “search” tool that queries an Agentset namespace using the platform’s powerful vector search capabilities. The MCP implementation handles authentication via API keys, supports multi‑tenant setups with a tenant identifier, and lets users override the tool description to match their domain language. When invoked by an assistant, the server returns a list of ranked documents along with metadata, enabling the model to ground its responses in real‑time data. This tight coupling is valuable for developers building knowledge‑rich applications such as customer support bots, research assistants, or compliance checkers that must reference up‑to‑date policy documents.

Key features include:

• Namespace awareness – each MCP instance can target a specific Agentset namespace, ensuring that only relevant data is surfaced.
• Customizable tool descriptions – developers can tailor the prompt used to describe the search tool, improving clarity for the AI assistant.
• Tenant support – a dedicated tenant ID allows multi‑tenant deployments, keeping data isolated across organizations.
• Seamless Claude integration – the MCP configuration is intentionally lightweight, requiring only a command and environment variables to register with Claude.

Real‑world use cases span from enterprise knowledge bases, where employees can ask questions that pull directly from internal documentation, to educational platforms that provide students with instant access to course materials. In a compliance scenario, an assistant could query the latest regulatory documents before generating policy‑advice, ensuring that responses are both accurate and legally current.

Because the server adheres to the MCP standard, it fits naturally into existing AI workflows. Developers can spin up an Agentset MCP instance locally or in the cloud, register it with their chosen assistant, and then focus on designing higher‑level prompts or chaining multiple tools. The result is a modular, scalable architecture where the heavy lifting of document retrieval and relevance ranking is handled by Agentset, while the AI model concentrates on reasoning and natural‑language generation.