Eka Care's Model Context Protocol (MCP) server facilitates interaction with medical knowledge-bases specifically curated for the Indian healthcare
Eka mcp server lets Claude, Cursor and other MCP clients work with Eka directly. Eka Care's Model Context Protocol (MCP) server facilitates interaction with medical knowledge-bases specifically curated for the Indian healthcare context. While advanced models from Claude, OpenAI, and others can perform adequately in medical contexts, their responses often lack grounding in.
Eka Care's Model Context Protocol (MCP) server facilitates interaction with medical knowledge-bases specifically curated for the Indian healthcare context. While advanced models from Claude, OpenAI, and others can perform adequately in medical contexts, their responses often lack grounding in factual information and published references. Additionally, India faces a significant challenge with the absence of centralized repositories for branded medications in the public domain.
The server is distributed via npm as @modelcontextprotocol/inspector, so most clients can run it without a manual build step. Add it to your MCP client's configuration and restart the client to pick it up — the copy-paste configs for Claude Desktop, Claude Code and Cursor are on this page.
AI-service servers chain other models into your assistant, turning a single chat into a small production pipeline. Eka sits in that group. Worth comparing against the other ai services servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
{
"mcpServers": {
"eka-mcp-server": {
"command": "uvx",
"args": [
"eka_mcp_server",
"--eka-api-host",
"https://api.eka.care",
"--client-id",
"<client_id>",
"--client-secret",
"<client_secret>"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.