MCPによるコード実行：より効率的なエージェントの構築

Engineering at AnthropicCode execution with MCP: Building more efficient agents

Direct tool calls consume context for each definition and result. Agents scale better by writing code to call tools instead. Here's how it works with MCP.

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.

Since launching MCP in November 2024, adoption has been rapid: the community has built thousands of MCP servers, SDKs are available for all major programming languages, and the industry has adopted MCP as the de-facto standard for connecting agents to tools and data.

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.

In this blog we'll explore how code execution can enable agents to interact with MCP servers more efficiently, handling more tools while using fewer tokens.

Excessive token consumption from tools makes agents less efficient

As MCP usage scales, there are two common patterns that can increase agent cost and latency:

Tool definitions overload the context window;

Intermediate tool results consume additional tokens.

1. Tool definitions overload the context window

Most MCP clients load all tool definitions upfront directly into context, exposing them to the model using a direct tool-calling syntax. These tool definitions might look like:

gdrive.getDocument Description: Retrieves a document from Google Drive Parameters: documentId (required, string): The ID of the document to retrieve fields (optional, string): Specific fields to return Returns: Document object with title, body content, metadata, permissions, etc.Copysalesforce.updateRecord Description: Updates a record in Salesforce Parameters: objectType (required, string): Type of Salesforce object (Lead, Contact, Account, etc.) recordId (required, string): The ID of the record to update data (required, object): Fields to update with their new values Returns: Updated record object with confirmationCopyTool 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.

1. Intermediate tool results consume additional tokens

Most MCP clients allow models to directly call MCP tools. For example, you might ask your agent: "Download my meeting transcript from Google Drive and attach it to the Salesforce lead."

The model will make calls like:

TOOL CALL: gdrive.getDocument(documentId: "abc123") → returns "Discussed Q4 goals...\n[full transcript text]" (loaded into model context) TOOL CALL: salesforce.updateRecord( objectType: "SalesMeeting", recordId: "00Q5f000001abcXYZ", data: { "Notes": "Discussed Q4 goals...\n[full transcript text written out]" } ) (model needs to write entire transcript into context again)CopyEvery intermediate result must pass through the model. In this example, the full call transcript flows through twice. For a 2-hour sales meeting, that could mean processing an additional 50,000 tokens. Even larger documents may exceed context window limits, breaking the workflow.

With large documents or complex data structures, models may be more likely to make mistakes when copying data between tool calls.

Code execution with MCP improves context efficiency

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.

There are a number of ways to do this. One approach is to generate a file tree of all available tools from connected MCP servers. Here's an implementation using TypeScript:

servers ├── google-drive │ ├── getDocument.ts │ ├── ... (other tools) │ └── index.ts ├── salesforce │ ├── updateRecord.ts │ ├── ... (other tools) │ └── index.ts └── ... (other servers)CopyThen each tool corresponds to a file, something like:

// ./servers/google-drive/getDocument.ts import { callMCPTool } from "../../../client.js"; interface GetDocumentInput { documentId: string; } interface GetDocumentResponse { content: string; } /* Read a document from Google Drive */ export async function getDocument(input: GetDocumentInput): Promise<GetDocumentResponse> { r