mcp

The Model Context Protocol connects AI to external systems by letting AI hosts discover and invoke tools, access resources, and use prompts exposed by MCP servers. It uses stdio or HTTP transports for flexible local and remote deployment.

You build an MCP server to expose custom tools using the provided Python or TypeScript SDKs. The server defines tools, resources, and prompts that AI hosts can discover and invoke via stdio or HTTP transport layers.

Yes, you can use Python or TypeScript to develop MCP clients and servers. The MCP implementation supports both SDKs, enabling you to build integrations that allow AI applications to discover external tools and data sources.

The best way to standardize AI integration with external data sources is using the Model Context Protocol. It provides a standardized protocol for AI hosts to uniformly access resources, execute tools, and utilize prompts from MCP servers.

You need HTTP+SSE transport if you want to expose an MCP server remotely, but local deployments can use stdio transport instead. MCP supports both transport layers to give you flexible deployment options for AI integration.

An AI agent using MCP can perform operations like accessing an internal knowledge base as a resource, executing a database query as a tool, and generating a report based on findings using prompts exposed by the MCP server.