#mcp

12 posts tagged with #mcp

Every article below is hand-written, technically reviewed, and focused on mcp. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.

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CLI-Anything: Make Any Software Agent-Native [2026]

CLI-Anything wraps any app in a thin command-line shim so AI agents can drive it like grep or git. Here's how it works, why CLIs beat prompt DSLs, and where it fits next to MCP.

hacker terminal screen hex code disassembler — illustration for article on REA: Reverse Engineer Anything With Technology

REA: Reverse Engineer Anything With AI Agents [2026]

REA is the open-source MCP server that lets Claude Code, Codex, and Cursor reverse-engineer binaries, Electron apps, APKs, and live browsers. Here's what it does, how to set it up, and the legal line nobody else is drawing.

A computer screen displaying network configuration code in a terminal window Developer Tools

MCP vs Function Calling in Agents [2026]: When to Say No

MCP backlash is real. Here’s my decision framework for when to use MCP vs function calling in agents, and when a bespoke tool API beats both on ops, security, and debugging.

A wooden block that says token sitting on a table Cybersecurity

MCP OAuth Security: Tool Impersonation, aud Mismatch, Token Replay [2026]

A threat model for MCP tool servers using OAuth: how tool impersonation and audience mismatch happen, how tokens get replayed, and what to validate and log in 2026.

A laptop screen displaying a web development tutorial website on a dark desk Frontend and Mobile

How to Build a Browser Based AI Agent (Safe-by-Default) [2026]

A practical browser AI agent architecture that stays local: Worker planner, Wasm sandbox, IndexedDB memory, and a service worker tool bridge with real guardrails.

a clipboard with a checklist on it next to a cup of coffee and Cybersecurity

MCP Server Security Best Practices: Checklist + CI Linter [2026]

A practical MCP server security test plan: authn/authz models, tool allowlists, prompt-injection regression tests, rate limits, audit logs, and a CI-friendly permissions linter.

an open laptop computer sitting on top of a table Cybersecurity

How to Implement OWASP Agentic Top 10 Controls [2026]

A control-by-control guide to OWASP agentic top 10 controls: where to gate tool calls (MCP/client/server), what to log, what to block, and what belongs in CI vs runtime.

Facebook login screen with email and password fields Developer Tools

How to Secure MCP Servers: Auth + AuthZ [2026 Tutorial]

A gateway-grade blueprint for MCP servers: authenticated discovery, PKCE/OAuth done right, tool-scoped permissions, audit logs, rate limits, and secret handling—with runnable Node/Express code.

padlock on laptop with light trails Cybersecurity

LLM Supply Chain Security Checklist: Lock Down Agents [2026]

A CI-ready checklist to treat models, MCP servers, tool plugins, and prompt packs as real dependencies. Pin, sign, attest, sandbox, and monitor before your agent ships malware.

claude code terminal laptop screen — illustration for article on Claude Code Security [2026]: Risks, Safe Technology

Claude Code Security [2026]: Risks, Safe Setup, Team Policy

Claude Code is safe only if you treat it like a junior engineer with terminal access. Here’s the 2026 playbook: permissions, sandboxing, egress controls, MCP allowlists, retention settings, and incident response.

MCP vs OpenAI Function Calling 2026: Which Tool Protocol Wins? AI and Machine Learning

MCP vs OpenAI Function Calling 2026: Which Tool Protocol Wins?

MCP wins for multi-model, cross-vendor agent ecosystems; OpenAI function calling wins for teams already deep in the OpenAI stack. Your choice depends on how vendor-locked you're willing to be.

MCP: The USB-C of AI — How Model Context Protocol Is Connecting Everything AI and Machine Learning

MCP: The USB-C of AI — How Model Context Protocol Is Connecting Everything

From a quiet Anthropic open-source release to 100 million downloads per month, MCP is becoming the universal standard for connecting AI agents to tools and data.

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