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oMLX MCP is a bridge that exposes your local oMLX server as Model Context Protocol tools. The mcp_omlx package connects Claude Desktop to the models running on your Mac and gives you seven tools to list models, load and unload them from memory, and run inference without leaving the client.
Your own MCP server exposes your data and functions to any AI assistant through the Model Context Protocol. With the official Python SDK and its FastMCP class you write a server in about twenty lines: you define tools, resources and prompts as type-annotated functions, and you connect it to Claude, Cline or Goose over stdio or HTTP.
Docker Agent is a Docker CLI plugin for building and running AI agents from a declarative YAML file, no code required: you define one or more agents, give them MCP tools, run them with docker agent run, and share them through any OCI registry, on OpenAI, Anthropic, Gemini or other providers.
This guide shows how to build a production-ready agent with the Anthropic SDK in Python: the tool-use loop with the Messages API, streaming with backpressure via a bounded queue, prompt caching with cache_control, your own MCP server registered with the Claude Agent SDK, OTel GenAI traces, and a non-root Docker container ready for production.
After eighteen months of multi-vendor adoption, MCP is the de facto standard for connecting models to tools. The complete guide: architecture, servers, policies, authentication, composition, and the antipatterns we’ve already seen in production.
The Model Context Protocol, proposed by Anthropic in late 2024 and adopted through 2025-2026 by Anthropic, OpenAI, Google, and the open-source community, already has proven operational patterns: separating generic servers from custom ones, explicit per-tool policies, credentials kept outside the model, prefixed composition, and contract tests. This is the state of the art in 2026.
The Model Context Protocol has gone from proposal to de facto standard for connecting editors with external tools. This practical guide walks through standing up a local MCP server, wiring it into VS Code or your client of choice, and understanding exactly what you are exposing.
Twenty months after the initial announcement, Model Context Protocol went from curiosity to de-facto standard among agent clients and servers. What is available, which servers are worth it, which problems remain open, and how it compares to earlier protocol maps.
Six months after A2A landed at the Linux Foundation, and after several implementation cycles from Google, Microsoft, and open projects, what version 1 of the protocol means and whether it is safe to build on yet.
With MCP solving the agent-to-tool layer, a parallel problem surfaces: how do two agents from different vendors communicate with each other. Google's Agent2Agent protocol, donated to the Linux Foundation in June 2025, tries to fill that gap with an open standard.
Model Context Protocol turns ten months old since Anthropic's announcement, and it is no longer just a proposal: hundreds of servers, cross-vendor implementations and a public registry now back it. A look at what has worked, what is still weak, and why 2025 marks the shift from curiosity to basic infrastructure.
MCP clients are now built into the editor itself: VS Code, Zed, Cursor, and several Neovim forks ship native support. The agent picks up project context in place, with no copying of files into a separate chat window. The practical questions become which servers to keep enabled and how to scope their permissions.
After more than a thousand community MCP servers appeared, the shortlist worth keeping stays small. The five official Anthropic servers in daily use here are filesystem, git, read-only Postgres, fetch, and memory. Servers wrapping Slack, email, or broad SaaS APIs open more attack surface than they repay, and tool-chaining between servers remains unsolved.
LLM agent security became a real incident category the moment assistants gained tool access. Agents that call tools expose a far wider attack surface than chatbots, and it now carries assigned CVEs, published audit reports, and its own OWASP Top 10. The spread of the Model Context Protocol and agent-driven corporate workflows built that surface in under 18 months.
Model Context Protocol (MCP) is the open standard Anthropic published on 25 November 2024 to connect language models with external data and tools over JSON-RPC 2.0. It does not replace function calling: it standardises the server side, aiming to become for context what the Language Server Protocol is for code editors.
