Codex is one of the best coding agents out there, and the MCP experience can be 10x. However, you have to be careful about MCPs, as they may have serious limitations.
Context pollution: When you overwhelm the models with too many MCP servers with unnecessary tools.
Programmatic tool chaining: While not a flaw of MCP itself, it is where LLMs can write their own glue code to connect to API endpoints. This actually improves execution reliability and reduces the number of trips to and from multiple tool calls, which, again, overwhelms the context window.
Handling large responses: LLMs can write code to parse and retrieve information from the file system. Instead of dumping information right into its context, efficient response handling can improve LLM performance and token consumption.
How are we solving this?
So, Composio MCP is a developer-focused product that essentially handles all the above by default.
On-demand tool loading so LLMs use only the tools they need at runtime, reducing context bloat.
a remote workbench so GPT models can write Composio code to chain multiple tools to complete tasks.
Large response handling. Large tool outputs are handled outside the LLM's context for an efficient
Rube is the consumer/prosumer MCP that scaffolds the core developer product, and in this guide, we will use Rube. The process for adding MCPs to Codex for other HTTP MCPs is more or less the same, and we will also see how to set up Stdio servers.