Eka MCP Server Overview

Eka MCP Server fills a critical gap in the Indian healthcare ecosystem by providing AI assistants with grounded, authoritative medical knowledge that is otherwise difficult to access. While large language models can generate plausible medical responses, they often lack verifiable references and may hallucinate facts—an unacceptable risk when dealing with patient care. The server supplies structured, curated datasets that reflect the unique regulatory and pharmaceutical landscape of India, ensuring that AI outputs are both accurate and compliant with local standards.

At its core, the server exposes two powerful tools. The Indian Branded Drug Search gives instant access to metadata for over 500,000 branded medications available in India. When an assistant queries a drug name or code, the tool returns detailed information—including generic composition, manufacturer details, and regulatory status—allowing the model to enrich its responses with precise, up‑to‑date data. The Indian Treatment Protocol Search grants contextual lookup of more than 180 treatment guidelines issued by respected Indian authorities such as the ICMR and RSSDI. This enables assistants to reference specific protocols when advising on diagnosis, management plans, or medication regimens.

For developers building AI‑powered healthcare solutions, these capabilities translate into several tangible benefits. Medical accuracy is dramatically improved because the assistant can cite real documents and drug registries rather than relying on inferred knowledge. Workflow efficiency is enhanced; users no longer need to switch between the assistant and external medical portals, as all necessary information is retrieved in a single prompt. The server’s reliance on curated data also reduces hallucinations, providing clinicians and patients with confidence in the AI’s recommendations. Moreover, because Eka MCP Server adheres to the open Model Context Protocol standard, it can be integrated seamlessly into any existing AI workflow that supports MCP—whether on Claude Desktop, custom in‑house systems, or other LLM platforms.

Typical use cases include clinical decision support tools for primary care physicians, telemedicine chatbots that must reference national guidelines, or pharmaceutical recommendation engines that need up‑to‑date drug information. In research settings, the server can serve as a reliable data source for training or fine‑tuning models on Indian medical literature. Its unique advantage lies in the combination of breadth (half a million drugs) and depth (official treatment protocols), all delivered through a lightweight, standard‑compliant interface that developers can adopt with minimal friction.