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.

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salesforce.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 confirmation

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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.

2. 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: