The Model Context Protocol (MCP) is an open standard for connecting AI agents to external systems. Connecting agents to tools and data traditionally requires a custom integration for each pairing, creating fragmentation and duplicated effort that makes it difficult to scale truly connected systems. MCP provides a universal protocol—developers implement MCP once in their agent and it unlocks an entire ecosystem of integrations.
Today developers routinely build agents with access to hundreds or thousands of tools across dozens of MCP servers. However, as the number of connected tools grows, loading all tool definitions upfront and passing intermediate results through the context window slows down agents and increases costs.
Most MCP clients load all tool definitions upfront directly into context, exposing them to the model using a direct tool-calling syntax.
Tool descriptions occupy more context window space, increasing response time and costs. In cases where agents are connected to thousands of tools, they’ll need to process hundreds of thousands of tokens before reading a request.
With code execution environments becoming more common for agents, a solution is to present MCP servers as code APIs rather than direct tool calls. The agent can then write code to interact with MCP servers. This approach addresses both challenges: agents can load only the tools they need and process data in the execution environment before passing results back to the model.
LLMs are adept at writing code and developers should take advantage of this strength to build agents that interact with MCP servers more efficiently.
Note that code execution introduces its own complexity. Running agent-generated code requires a secure execution environment with appropriate sandboxing, resource limits, and monitoring. These infrastructure requirements add operational overhead and security considerations that direct tool calls avoid. The benefits of code execution—reduced token costs, lower latency, and improved tool composition—should be weighed against these implementation costs.
Although many of the problems here feel novel—context management, tool composition, state persistence—they have known solutions from software engineering. Code execution applies these established patterns to agents, letting them use familiar programming constructs to interact with MCP servers more efficiently.