MCP-as-Code-API

Problem

Conventional tool calling loads every advertised tool schema into the system prompt and routes every tool result back through the model's context window, even when the model is only going to pass that result straight to the next tool. A single workflow that joins a 5 megabyte spreadsheet with a paginated Slack thread can burn six-figure token counts before any actual reasoning happens, and most of those tokens are plumbing the model never has to read.

Solution

At connection time, walk each MCP server's tool list and emit a file per tool (e.g. servers/gdrive/getDocument.ts, servers/slack/listChannels.ts) with full type signatures derived from the JSON schema. Expose this tree to the agent as a readable filesystem and let it explore via standard list/read primitives rather than loaded schemas. The agent then writes execution code — a short script that imports the wrappers, chains calls, transforms results in-memory, and prints only the final answer. Tool outputs live in sandbox variables; only what the script prints (or saves to a designated output) crosses back into model context. Pair with progressive disclosure: the model reads only the tool files it intends to use.

When to use

• Workflows chain many MCP tools and intermediate data is large.
• A code-execution sandbox is already part of the agent stack.
• Token cost or latency dominated by tool-output round-tripping.
• Tool surface is too large to fit all schemas in prompt.