Open-source MCP suite improves Claude Code generation quality by 15-20%

What this MCP suite does

This is an open-source suite of three local MCP servers and a prompt skill designed to improve AI code generation quality, specifically addressing the 'bad token' problem where agents make poor early design choices and then hallucinate extensive code to justify them, breaking existing architecture.

Key components

• Lad: A dual-reviewer system that uses OpenRouter to send code diffs to two independent models (examples given: Kimi-2.5, GLM-5) for design and code review, breaking the autoregressive self-gaslighting loop.
• Serena: A headless IDE that indexes your codebase, allowing review models to check code against your project's historical requirements and architecture.
• Kindly: A web search tool that hooks directly into StackExchange and GitHub APIs, feeding the agent entire conversations (question, code snippets, accepted answers, comments) in one LLM-native chunk instead of standard HTML scraping.
• TDD Skill: A workflow override that forces the agent to write a REQUIREMENTS.md file, ask clarifying questions, and write tests before writing feature code.

Performance and implementation

One customer, Relevant Software, reported that adopting this suite increased their AI code generation quality with Claude Code by 15-20%. The suite runs locally over stdio and is compatible with Claude Code, Cursor, and Codex. The source mentions it helps significantly with cutting-edge technologies like LangChain, LangGraph, and LangSmith.