Build an AI Workflow with MCP: A Small, Testable Python Example
Build a local MCP tool, call it from a Python client, and verify its boundaries before connecting a model or a production system.
8 min read
Comparisons, setup guides, and software decisions for independent developers and small teams.
Build a local MCP tool, call it from a Python client, and verify its boundaries before connecting a model or a production system.
8 min read
A practical shortlist for repository exploration, feature work, and code review, with a small trial you can run before subscribing.
Choose an AI API around your workload, failure handling, data boundaries, and cost per accepted result—not a model leaderboard.
Build a stack in stages: ship a useful product, protect customer data, then add automation and observability when specific problems justify them.
Give AI reviewers a bounded diff, repository rules, and a finding format, then verify the issues before a human merge decision.
Evaluate AI coding assistants with a repeatable task, acceptance criteria, and a downloadable worksheet. Compare correctness, review effort, and cost.
Design an AI workflow with clear inputs, review gates, retries, and failure cases. Includes a support-reply example and a downloadable pilot checklist.
A simple framework for thinking about usage, integration, maintenance, and the cost of switching.
We cover AI coding, automation, and the software behind small teams. Comparisons link to their sources. Tutorials explain the example and its limits.
About the publication