How 49 business apps handle MCP access control for AI-driven invoice tools

·1 views

A small business software team integrated Model Context Protocol (MCP) endpoints across 49 of their self-hosted applications following their 3.0 release. Rather than relying on instructions to prevent AI models from misusing data, they built access controls directly into the key-permission layer. Read-only API keys suppress write-capable tools entirely from the protocol's tool-list response, meaning an AI model is never even aware those options exist. All keys operate as named users within the existing role system, so access boundaries remain consistent whether a person or a model is making requests. Every action taken through a read-write key is recorded in the audit log, keeping accountability intact across both human and AI interactions.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

One Developer, Two Projects, Four Spec Standards: How Context Decided the Winner

A solo developer working on two side projects — a cat-tracking app and an '80s-style arcade game — evaluated four specification-driven development standards: Spec Kit, Kiro (AWS), PRD plus ADR, and OpenSpec. Each standard was assessed against the specific risks and needs of each project rather than on abstract merit. The cat app, which had well-defined requirements upfront, adopted OpenSpec for its stable baseline approach, borrowing the Constitution Check from Spec Kit and ADR logging from the Nygard format. The arcade game, with its fluid and evolving requirements, suited Spec Kit better due to its gated milestone workflow that enforces structured progression through specification, planning, and tasks. The key takeaway is that choosing a specification standard depends on a project's primary risk — requirement drift versus requirement discovery — rather than any universal ranking of the formats.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why True Decoupling Requires Isolating Data, Not Just Logic

A software architecture analysis argues that most engineers misunderstand decoupling by focusing on code structure while ignoring how data is shared in memory. The core argument holds that if multiple modules point to the same mutable memory block, they remain tightly coupled regardless of how cleanly their interfaces are designed. The piece introduces the concept of 'stop-and-start boundaries,' where data is serialized and handed off across explicit boundaries rather than shared via live pointers. Languages like Erlang, Clojure, and Rust are cited as practical examples of enforcing true data isolation through process-level copying, structural sharing, and compile-time ownership rules respectively. The author contends that adopting data-centric decoupling not only improves system reliability but also addresses limitations in how both human developers and AI tools reason about large codebases.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How Switching from List to Set Cuts Duplicate-Check Complexity in Python

A developer working on a classic LeetCode problem — detecting duplicates in an integer array — initially used a Python list for lookups, assuming it would run in O(n) time. The approach was actually O(n²) because checking membership in a list requires scanning each element sequentially. Replacing the list with a set brought the solution to true O(n) time, since sets use hash-based lookups. The developer further simplified the logic to a single line: comparing the length of the original array against its set equivalent. The exercise highlights how a seemingly minor data structure choice can carry significant hidden performance costs.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Dev Uses AI-Assisted Stress Testing to Catch Bugs Before Users Do

A software developer and former clinician describes how years of manual application testing shaped a rigorous approach to validation, including using AI models from multiple companies to generate edge-case scenarios. The habit began during a job as an application analyst, where he spent months manually timing image-load performance on a PACS system to document a problem users could only describe anecdotally. He found measurable degradation but no clear pattern — a result he considers equally valuable, as it prevented false conclusions. Today he deliberately prompts AI tools to surface unexpected inputs and failure modes rather than asking them to judge overall quality. This philosophy led him to build a dedicated tool called ReliAgent, designed to address the unique reliability challenges of AI agent systems where components may function correctly yet still produce untrustworthy results.

0 comments Read more at DEV Community