3 posts tagged with "claude"

Your AI assistant is only as useful as the context it can reach. Ask Claude to summarize your latest GitHub PR and it draws a blank — unless you've given it a way in. Ask ChatGPT to pull last week's Slack thread and it can't — unless it's connected.

Model Context Protocol (MCP) is the open standard that changes this. Originally developed by Anthropic, now adopted by OpenAI, Google DeepMind, and the broader AI ecosystem — it lets any compatible model connect to any compatible tool through a single, consistent interface. One protocol. Any model. Any tool.

Most "AI" projects are still just API calls wrapped in if/else logic. True agentic AI gives the model real tools — file access, database queries, API calls — and lets it decide how to use them to accomplish a goal.

Model Context Protocol (MCP), developed by Anthropic, is the emerging open standard for connecting AI agents to those tools in a secure, structured way. In this guide you'll configure two MCP servers, write a simple agent, and automate a daily reporting task — using Claude, OpenAI, or a self-hosted Ollama model.

LLM costs in production pipelines scale differently from anything else in your data infrastructure. A poorly architected pipeline that sends every event through GPT-4o can burn through thousands of dollars per day. A well-architected one running the same workload might cost a tenth of that — by routing each task to the model that's just capable enough for the job.

This post covers the cost architecture decisions that keep AI pipelines economically viable at scale.