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kailash-mcp

Expose MCP tools and resources to AI agents via multiple transports.

Updated Apr 2, 2026
Search Tags:#authentication#mcp#model-context-protocol#mcp-server#progress-reporting#transports#structured-tools
One-click install
npx skills add https://github.com/ssssssssassssss/disease-risk-classifier --skill kailash-mcp-ssssssssassssss
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kailash-mcp
Source: https://github.com/ssssssssassssss/disease-risk-classifier/tree/main/.claude/skills/05-kailash-mcp
Command: npx skills add https://github.com/ssssssssassssss/disease-risk-classifier --skill kailash-mcp-ssssssssassssss

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Exposes Model Context Protocol (MCP) servers so AI agents can call structured, type-safe tools and access managed resources without brittle ad-hoc integrations, reducing integration complexity and runtime failures.

Core Features & Use Cases

  • Full MCP Specification: Implements MCP transports (stdio, SSE, HTTP, WebSocket) and tool/resource discovery for agent integrations.
  • Secure Authentication & Operations: Supports API keys, JWT, OAuth 2.1, HMAC patterns, and production security best practices.
  • Structured Tools & Validation: JSON Schema-based input/output validation, automatic schema generation from type hints, progress reporting, and resource templates for RAG and data access.
  • Testing & Production Patterns: Clear guidance for unit, integration, and E2E tests with real vs mock MCP execution and multi-server configurations.

Quick Start

Start a local MCP server named my-server, register a summarize tool that returns a summary string, and run the server so an agent can call the tool.

Frequently Asked Questions about kailash-mcp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I expose structured tools to AI agents using the Model Context Protocol?▼

You expose structured tools to AI agents using the Model Context Protocol by deploying an MCP server with JSON Schema input and output validation, ensuring type-safe tool execution and managed resource access without brittle ad-hoc integrations.

What authentication methods does MCP support for production AI agent servers?▼

MCP supports API keys, JWT, OAuth 2.1, and HMAC authentication patterns for production AI agent servers, enabling secure operations and access control across local development, web clients, and production services.

Can I run an MCP server over HTTP and WebSocket transports simultaneously?▼

Yes, you can run an MCP server over HTTP and WebSocket transports simultaneously, as the full MCP specification supports configuring stdio, SSE, HTTP, and WebSocket transports for diverse agent integrations and production services.

How do I validate tool inputs and outputs in an MCP server implementation?▼

You validate tool inputs and outputs in an MCP server implementation using JSON Schema-based validation, which includes automatic schema generation from Python type hints to ensure structured, type-safe tool execution and reduce runtime failures.

Does the MCP server support progress reporting and resource templates for RAG?▼

Yes, the MCP server supports progress reporting and resource templates for RAG and data access, allowing AI agents to receive execution updates and access managed resources through structured, type-safe tool calls.

What Python version is required to implement Kailash Core SDK compatible MCP tools?▼

Python 3.8 or higher is required to implement Kailash Core SDK 0.9.25+ compatible MCP tools, with testing patterns available for unit, integration, and E2E tests using real or mock MCP execution.

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