Articles tagged “mcp”
30 articles
7 FastMCP mistakes that break your agent in production
FastMCP servers that work locally often fail at scale. Seven common mistakes, from missing annotations to monolithic tool sets, and how to fix each one.
Your MCP server is a monolith. Here's how to fix it
MCP servers dump every tool into the context window, burning tokens before your agent reasons. Four patterns to fix it: decompose, filter, gateway, facade.
The no-code ceiling: when agent builders hit production
Visual agent builders get you to 80% fast. The last 20%, telephony, monitoring, testing, and memory, requires infrastructure they never intended to provide.
Build the MCP + A2A agent protocol stack from scratch
Wire an MCP server to an A2A agent that delegates tasks and calls tools. TypeScript and Python examples, Streamable HTTP transport, Agent Cards, and auth.
Your AI Agent's Context Window Is Already Half Full
System prompts, tool schemas, MCP descriptions, memory injection, conversation history. They all eat tokens before the user says a word. Learn where your context budget goes and how to manage it.
MCP vs A2A: Tools Protocol, Agents Protocol, and Why You Need Both
MCP connects agents to tools. A2A connects agents to each other. Most developers confuse them. This guide breaks down both protocols with architecture diagrams, real code, and a decision framework for production systems.
The Insurance Agent That Never Misquotes a Policy
How regional insurers deploy AI agents that answer policy questions accurately, intake claims end-to-end, and produce the audit trail regulators demand.
Build a Restaurant AI That Remembers Every Regular
Build an AI phone agent for a local restaurant that takes orders, answers menu questions, and remembers regulars. A developer side hustle worth $400/month per client.
50 Tools, Zero Memory. The Biggest Gap in AI Agents Today
AI agents can call 50 APIs but can't remember what you said yesterday. The tool layer is years ahead of the memory layer, and customers are paying the price.
Why MCP Exists: Tool Calling Shouldn't Need Adapter Code
OpenAI, Anthropic, and Google all implement function calling differently. MCP is emerging as the standard that saves developers from writing adapter code for every provider.
From Keyword Search to Shopping Memory
Build the intelligence layer for an AI shopping assistant: semantic product search with Commerce MCP, customer memory that persists across visits, and MCP tool registration for multi-channel deployment.
Why AI Shopping Still Feels Like a Search Bar
Most AI shopping assistants return walls of text. Learn how ChatKit widgets and Vercel AI SDK structured output turn AI recommendations into interactive product cards with images, prices, and add-to-cart buttons.
Context Engineering Is What Your Agent Actually Needs
Prompt engineering hits a wall with production AI agents. Context engineering fixes it. Build a full context pipeline with memory, RAG, history compression, and tool resolution.
The Three Protocols Every AI Agent Will Speak
The AI agent protocol stack has three layers: MCP for tools, A2A for agent-to-agent communication, and WebMCP for browser interaction. A practitioner's guide to how they work together in production.
Your AI Agent Has No Guardrails
Air Canada honored a refund its chatbot hallucinated. DPD's bot cursed at customers on camera. One e-commerce agent approved $2.3M in unauthorized refunds at 2:47 AM. Here is the five-layer guardrail architecture that prevents all three.
Every Tool Is an Injection Surface
Prompt injection moved from chat to tool calls. Anthropic, OpenAI, and Arcjet shipped defenses in the same month. Here's what changed, what works, and what your agent architecture needs now.
Why Browser Agents Waste 89% of Their Tokens
Browser agents burn 1,500-2,000 tokens per screenshot. Chrome 146's navigator.modelContext API lets websites expose structured tools instead, cutting token usage by 89% and raising task accuracy to 98%. Here's how WebMCP works.
Parte 1: Los 7 Puntos de Extensión de Claude — El Modelo Mental
CLAUDE.md, Skills, Hooks, MCP Servers, Connectors, Claude Apps, Plugins — el ecosistema de extensiones de Claude es poderoso pero confuso. Aquí está el modelo mental que le da sentido a los 7.
Parte 3: MCP Servers vs. Connectors vs. Apps
Todas las Claude Apps son Connectors. Todos los Connectors son MCP Servers. Entender esta jerarquía — y cuándo construir vs. usar integraciones administradas — ahorra semanas de ingeniería innecesaria.
Parte 4: Los 7 Puntos de Extensión en una Base de Código de Producción
Más de 50 skills, múltiples MCP servers, reglas con alcance, hooks de seguridad — así es como los 7 puntos de extensión de Claude se componen en un monorepo NestJS real con 17 proyectos. Qué funciona, qué entra en conflicto y qué haríamos diferente.
MCP es ahora el estandar de la industria para integraciones de agentes de IA. Esto es lo que significa
MCP estandariza como los agentes de IA se conectan a herramientas y datos, reemplazando integraciones fragiles y propietarias con un protocolo universal. Esto es lo que significa para tus agentes.
Your Voice Agent Forgets Everything. Here's How to Fix That
How to add persistent memory, tools, and knowledge to Pipecat and LiveKit voice agents using the Chanl Python SDK — one SDK instead of assembling five services.
71% of organizations aren't prepared to secure their AI agents' tools
MCP gives AI agents autonomous access to real systems — and introduces attack vectors that traditional security can't see. A technical breakdown of tool poisoning, rug pulls, cross-server shadowing, and the defense framework production teams need now.
MCP Streamable HTTP: The Transport Layer That Makes AI Agents Production-Ready
MCP's Streamable HTTP transport replaced the original SSE transport to fix critical production gaps. This guide covers what changed, why it matters, and how to implement it in TypeScript with code examples.
Your agent has 30 tools and no idea when to use them
MCP tools give agents external capabilities. Skills give agents behavioral expertise. Learn the architecture of both, build them in TypeScript, and understand when to use each — and when you need both.
Herramientas para Agentes de IA: MCP, OpenAPI y Gestión de Herramientas que Realmente Escala
Cómo los agentes de IA en producción descubren, ejecutan y gestionan herramientas: desde el protocolo MCP hasta la importación automática de OpenAPI, sandboxing de seguridad e infraestructura de herramientas multi-tenant.
Construye tu propio sistema de herramientas para agentes de IA: ¿qué se rompe cuando agregas la herramienta número 20?
Construye un sistema completo de herramientas para agentes de IA orientados al cliente desde cero: registro, ejecución, autenticación y monitoreo. Luego aprende qué se rompe cuando los clientes reales comienzan a llamar.
MCP Deep Dive: Advanced Patterns for Agent Tool Integration
Production MCP patterns for teams who've built their first server and need to scale it — OAuth 2.1 with PKCE, Streamable HTTP transport, gateways, sampling, dynamic tool registration, and multi-tenant security.
MCP Explicado: Construye Tu Primer Servidor MCP en TypeScript y Python
Construye un servidor MCP funcional desde cero en TypeScript y Python. Tutorial práctico que cubre tools, resources, transports y testing.
The MCP Marketplace Problem: Why Standardized Integrations Need Standardized Testing
5,800+ MCP servers, 43% with injection flaws. Standardized protocol doesn't mean standardized quality. Why every MCP integration needs automated testing.
Aprende IA Agéntica
Una lección por semana: técnicas prácticas para construir, probar y lanzar agentes IA. Desde ingeniería de prompts hasta monitoreo en producción. Aprende haciendo.
