Explore production-ready MCP servers, generate configuration files, and access the best learning resources to build intelligent agent workflows.
Allows AI agents to search, view, create, and manage GitHub repositories, issues, pulls, and commits.
Enables agents to inspect schemas, execute read-only queries, and analyze PostgreSQL database tables safely.
MCP server integration for Zerodha Kite Connect APIs, allowing agents to fetch holdings, margins, positions, and live quotes.
Integrate Brave Search to allow agents to search the web, fetch search results, and crawl web pages.
Exposes tools to download and convert remote HTML pages into clean, agent-readable markdown.
Allows agents to list channels, read conversations, read threads, and post messages to Slack workspaces.
Enables secure and sandboxed local directory read, write, search, and file information operations.
Persistent semantic knowledge graph storage using simple files to give agents long-term episodic memory.
Run 150+ AI apps — image, video, audio, LLMs, 3D and more. Browse, execute, stream results.
Remote MCP server for Tandem docs, install guides, SDKs, workflows, and agent setup help.
AI-powered trading strategy development: backtesting, market data, and portfolio analysis
Crypto market signals, technical indicators, and sentiment analysis for AI agents.
Select the tools you want to use, customize their configuration arguments, and copy the unified configuration file directly into your agent setup.
// Configuration will generate here...Save this code as mcp_config.json in your local app configuration directory (e.g., ~/Library/Application Support/Antigravity/mcp_config.json or your Claude Desktop config folder).
Writing custom MCP servers is simple. Create integrations in Node.js or Python to give your AI workspace access to proprietary systems and APIs.
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
// Initialize server
const server = new Server({
name: "custom-mcp-server",
version: "1.0.0"
}, {
capabilities: { tools: {} }
});
// Define a tool
server.setRequestHandler(ListToolsRequestSchema, async () => ({
tools: [{
name: "calculate_metric",
description: "Computes custom business analytics",
inputSchema: {
type: "object",
properties: {
id: { type: "string" }
},
required: ["id"]
}
}]
}));
// Connect standard input/output transport
const transport = new StdioServerTransport();
await server.connect(transport);The official protocol documentation, schemas, and API specification for building MCP clients and servers.
Comprehensive structured curriculum covering core architectures, prompts, resources, tools, and cross-language setups (Python, TS, Java, .NET).
Deep-dive video course covering client-server architecture, JSON-RPC, tools discovery, and practical flight booking demos.
A rapid developer-oriented walkthrough of configuring standard MCP servers and clients using TypeScript.
Approach-friendly tutorial that covers how to build a basic server and hook it up to local database tables.
Covers how to integrate web search engines and crawlers into your custom MCP servers to feed LLMs.
Step-by-step video tutorials for using Node.js and Python SDKs to create tool bindings and servers for LLMs.
Cohort-based advanced training on designing secure custom filesystem and database adapters for AI agent teams.
A comprehensive blog post on integrating third-party REST APIs into standard Model Context Protocol structures.
Microsoft's official open-source curriculum for MCP, providing cross-language examples in .NET (C#), Java, TypeScript, Rust, and Python.
Learn what MCP is, why it is crucial for AI tool-use, and walk through building a Python-based server step-by-step.