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.
Part 1: Claude's 7 Extension Points — The Mental Model
CLAUDE.md, Skills, Hooks, MCP Servers, Connectors, Claude Apps, Plugins — Claude's extension ecosystem is powerful but confusing. Here's the mental model that makes sense of all 7.
Part 3: MCP Servers vs. Connectors vs. Apps
All Claude Apps are Connectors. All Connectors are MCP Servers. Understanding this hierarchy — and when to build vs. use managed integrations — saves weeks of unnecessary engineering.
Part 4: All 7 Extension Points in One Production Codebase
50+ skills, multiple MCP servers, scoped rules, safety hooks — here's how all 7 Claude extension points compose in a real NestJS monorepo with 17 projects. What works, what fights, and what we'd do differently.
MCP Is Now the Industry Standard for AI Agent Integrations. Here's What That Means
MCP standardizes how AI agents connect to tools and data, replacing fragile, proprietary integrations with a universal protocol. Here's what it means for your agents.
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.
AI Agent Tools: MCP, OpenAPI, and Tool Management That Actually Scales
How production AI agents discover, execute, and manage tools — from MCP protocol to OpenAPI auto-importing, security sandboxing, and multi-tenant tool infrastructure.
Build your own AI agent tool system — what breaks when you add the 20th tool?
Build a complete tool system for customer-facing AI agents from scratch — registry, execution, auth, monitoring. Then learn what breaks when real customers start calling.
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 Explained: Build Your First MCP Server in TypeScript and Python
Build a working MCP server from scratch in TypeScript and Python. Hands-on tutorial covering tools, resources, transports, and 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.
Learn Agentic AI
One lesson a week — practical techniques for building, testing, and shipping AI agents. From prompt engineering to production monitoring. Learn by doing.
