MCP Code Execution Pattern

Expert knowledge for designing agent systems that generate and execute code to interact with MCP servers, instead of calling tools directly.

When to Use This Pattern

Use code execution when...	Use direct tool calls when...
Connecting to 10+ MCP servers or 50+ tools	Few servers with handful of tools
Intermediate results are large (>10K tokens)	Results are small and all needed by the model
Workflows need loops, retries, or conditionals	Linear sequences of 2-3 tool calls
PII must not reach the model context	No sensitive data in tool responses
Tasks benefit from state persistence across runs	Stateless, one-shot operations
You want agents to accumulate reusable skills	Fixed, predefined workflows

Core Architecture

How It Works

Instead of loading all MCP tool definitions into the model context upfront, the agent:

Discovers available tools by navigating a typed file tree
Generates TypeScript/Python code that imports and calls typed wrapper functions
Executes the code in a sandboxed environment
Returns only filtered/summarized results to the model

This reduces token usage from O(all_tool_definitions) to O(only_relevant_imports).

File Tree Structure

project/
├── servers/
│   ├── google-drive/
│   │   ├── getDocument.ts
│   │   ├── getSheet.ts
│   │   ├── listFiles.ts
│   │   └── index.ts          # Re-exports all tools
│   ├── salesforce/
│   │   ├── query.ts
│   │   ├── updateRecord.ts
│   │   └── index.ts
│   └── slack/
│       ├── sendMessage.ts
│       ├── getChannelHistory.ts
│       └── index.ts
├── skills/                    # Agent-accumulated reusable functions
│   └── save-sheet-as-csv.ts
├── workspace/                 # Persistent state between executions
├── client.ts                  # MCP client that routes calls to servers
└── sandbox.config.ts          # Execution environment configuration

Typed Wrapper Pattern

Each MCP tool gets a typed wrapper function that the agent imports:

typescript

// 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> {
  return callMCPTool<GetDocumentResponse>("google_drive__get_document", input);
}

The agent then writes code that uses these wrappers naturally:

typescript

import * as gdrive from "./servers/google-drive";
import * as salesforce from "./servers/salesforce";

const transcript = (
  await gdrive.getDocument({ documentId: "abc123" })
).content;

await salesforce.updateRecord({
  objectType: "SalesMeeting",
  recordId: "00Q5f000001abcXYZ",
  data: { Notes: transcript },
});

Key Patterns

1. Progressive Tool Discovery

The agent navigates the filesystem to find relevant tools on demand, instead of loading all definitions upfront.

Agent: "I need to read from Google Drive"
  → ls servers/
  → ls servers/google-drive/
  → cat servers/google-drive/getDocument.ts  (reads signature + JSDoc)
  → generates code importing only getDocument

Token impact: 150,000 tokens (all definitions) reduced to ~2,000 tokens (one definition). 98.7% reduction.

2. Context-Efficient Data Filtering

Filter large datasets in the execution environment before results reach the model:

typescript

// Filter in the sandbox — only summary reaches the model
const allRows = await gdrive.getSheet({ sheetId: "abc123" });
const pending = allRows.filter((row) => row["Status"] === "pending");
console.log(`Found ${pending.length} pending orders`);
console.log(pending.slice(0, 5)); // Only first 5 for model review

3. Native Control Flow

Replace chained tool calls with code-native loops and conditionals:

typescript

// Polling loop — runs entirely in sandbox
let found = false;
while (!found) {
  const messages = await slack.getChannelHistory({ channel: "C123456" });
  found = messages.some((m) => m.text.includes("deployment complete"));
  if (!found) await new Promise((r) => setTimeout(r, 5000));
}
console.log("Deployment notification received");

4. PII Tokenization

The MCP client intercepts responses and tokenizes sensitive data before it reaches the model:

typescript

// Agent writes this code
for (const row of sheet.rows) {
  await salesforce.updateRecord({
    objectType: "Lead",
    recordId: row.salesforceId,
    data: { Email: row.email, Phone: row.phone, Name: row.name },
  });
}
console.log(`Updated ${sheet.rows.length} leads`);

What the model sees in the execution output:

[
  { salesforceId: "00Q...", email: "[EMAIL_1]", phone: "[PHONE_1]", name: "[NAME_1]" },
  { salesforceId: "00Q...", email: "[EMAIL_2]", phone: "[PHONE_2]", name: "[NAME_2]" }
]
Updated 247 leads

The actual PII flows between external systems without entering model context.

5. State Persistence

Save intermediate results to the workspace for cross-execution continuity:

typescript

// Execution 1: fetch and save
const leads = await salesforce.query({
  query: "SELECT Id, Email FROM Lead LIMIT 1000",
});
await fs.writeFile("./workspace/leads.csv", leads.map((l) => `${l.Id},${l.Email}`).join("\n"));

// Execution 2: resume from saved state
const saved = await fs.readFile("./workspace/leads.csv", "utf-8");

6. Skill Accumulation

Agents persist reusable functions as skills for future executions:

typescript

// skills/save-sheet-as-csv.ts
import * as gdrive from "../servers/google-drive";
import * as fs from "fs/promises";

export async function saveSheetAsCsv(sheetId: string): Promise<string> {
  const data = await gdrive.getSheet({ sheetId });
  const csv = data.map((row) => row.join(",")).join("\n");
  const path = `./workspace/sheet-${sheetId}.csv`;
  await fs.writeFile(path, csv);
  return path;
}

Later executions import the skill directly:

typescript

import { saveSheetAsCsv } from "./skills/save-sheet-as-csv";
const csvPath = await saveSheetAsCsv("abc123");

Scaffolding a New Project

Step 1: Identify MCP Servers

List the MCP servers the agent needs to interact with. Check

.mcp.json

or the project's MCP configuration:

bash

cat .mcp.json 2>/dev/null || echo "No MCP config found"

Step 2: Generate Server Directory

For each MCP server, create a directory with typed wrappers. Each tool gets its own file with:

Input interface
Output interface
JSDoc comment describing the tool
Async function wrapping
```
callMCPTool
```

Step 3: Create the MCP Client

The client routes

callMCPTool

calls to the appropriate MCP server:

typescript

// client.ts
import { Client } from "@modelcontextprotocol/sdk/client/index.js";

const clients = new Map<string, Client>();

export async function callMCPTool<T>(
  toolName: string,
  input: Record<string, unknown>
): Promise<T> {
  const serverName = toolName.split("__")[0];
  const client = clients.get(serverName);
  if (!client) throw new Error(`No MCP client for server: ${serverName}`);

  const result = await client.callTool({ name: toolName, arguments: input });
  return result.content as T;
}

Step 4: Configure the Sandbox

The execution environment needs:

Concern	Requirement
Isolation	Process-level or container-level sandboxing
Resource limits	CPU time, memory caps, disk quotas
Network	Restrict to MCP server connections only
Timeout	Hard execution time limit per run
Filesystem	Scoped to `workspace/` and `servers/` directories
Monitoring	Log all executions and MCP calls

Step 5: Wire Up the Agent Loop

The agent loop becomes:

1. Receive user request
2. Agent explores servers/ tree to find relevant tools
3. Agent generates TypeScript code using typed wrappers
4. Code executes in sandbox
5. Filtered output returns to agent
6. Agent decides: done, or generate more code?

Security Checklist

Item	Status
Sandboxed execution environment	Required
Resource limits (CPU, memory, disk)	Required
Network isolation (MCP servers only)	Required
Execution timeout	Required
PII tokenization in MCP client	Recommended for sensitive data
Audit logging of all executions	Recommended
Read-only access to `servers/`	Recommended
Scoped write access to `workspace/` only	Recommended

Agentic Optimizations

Context	Approach
Many tools (50+)	Use progressive discovery via file tree
Large intermediate data	Filter in sandbox, return summaries
Multi-step workflows	Generate single code block with control flow
Sensitive data pipelines	Enable PII tokenization in MCP client
Long-running tasks	Use workspace/ for state persistence
Repeated operations	Extract to skills/ for reuse

Quick Reference

Token Impact

Approach	Tool definitions	Intermediate data	Total
Direct tool calls	All loaded upfront	Passes through context	High
Code execution	On-demand discovery	Stays in sandbox	Low

When NOT to Use This Pattern

Simple integrations with 1-3 MCP servers
All tool responses are small and needed by the model
No sensitive data in tool responses
Infrastructure complexity isn't justified (sandbox setup, monitoring)
Prototype or proof-of-concept stage

Reference

Anthropic Engineering: Code Execution with MCP
Cloudflare "Code Mode" — independent validation of the same pattern

mcp-code-execution

NPX Install

Tags

SKILL.md Content

MCP Code Execution Pattern

When to Use This Pattern

Core Architecture

How It Works

File Tree Structure

Typed Wrapper Pattern

Key Patterns

1. Progressive Tool Discovery

2. Context-Efficient Data Filtering

3. Native Control Flow

4. PII Tokenization

5. State Persistence

6. Skill Accumulation

Scaffolding a New Project

Step 1: Identify MCP Servers

Step 2: Generate Server Directory

Step 3: Create the MCP Client

Step 4: Configure the Sandbox

Step 5: Wire Up the Agent Loop

Security Checklist

Agentic Optimizations

Quick Reference

Token Impact

When NOT to Use This Pattern

Reference