Build an MCP Client for AI Agents: Config, Auth, Transport

Building an MCP-compatible client for AI agents requires 20% protocol compliance and 80% handling of edge cases. Each client, such as Cursor, Claude Desktop, Windsurf, OpenClaw, and Hermes, has a slightly different JSON structure for the same Streamable HTTP server. To ensure a single server works across all clients, the JSON shape must be generated per platform, with a single upstream URL, specific headers, normalized JSON-RPC parameters, and stateless clients that never send the initialize message.

The MCP client is searched about 1,600 times a month, but most readers need configuration rather than the specification. The key takeaway is that one server and one upstream string provide compatibility across all clients, making changes easier. The config shape is specific to each client, not the protocol itself, with differences in type, transport, URL, and server URL.

Two headers are crucial for authentication and tracing. Normalizing JSON-RPC parameters before validation ensures compatibility. A patch can achieve compatibility without forking the codebase. To improve integration, modify your client config to use generated output for configuration, auth headers, and the upstream URL. This approach addresses edge cases like 401 errors and empty tool lists while enabling per-agent audit of platform, key, and trace.

Understanding the MCP protocol is essential, but compatibility of the client configuration is the real challenge. The Model Context Protocol defines standard transports, with Streamable HTTP being the one that works over a network. The specification focuses on the client's behavior, leaving decisions about endpoints, transport keys, and config shapes to individual teams.

The result is inconsistency across clients, making it difficult for users who only want a working configuration. SmartGate, an MCP-native algorithm gateway, demonstrates the compatibility layer needed to support multiple clients. The provided code illustrates how to build configurations for different platforms, generate auth headers, and handle identity forwarding.

By treating client configuration as generated output and centralizing the upstream URL, the integration process becomes more manageable, and auditability improves across all AI agents.