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M MCP Servers Developer low

delimit-ai/delimit

by Various

Govern AI coding across Claude Code, Codex, Cursor, and Gemini CLI. Breaking-change detection, deploy gates, and multi-model consensus — delivered as MCP server, CLI, and GitHub Ac

D

MCP

delimit-ai/delimit

Added 1 June 2026

#ai-governance #api-governance #breaking-changes #claude-code #codex #cross-model #cursor #deliberation

Overview

Governs AI coding across multiple assistants like Claude Code, Codex, Cursor, and Gemini CLI. Detects breaking changes, enforces deploy gates, and runs multi-model consensus checks. Available as an MCP server, a CLI tool, and a GitHub Action.

Best for

Best for
Teams wanting to add governance and safety checks when using multiple AI coding assistants.

Use cases

  • Enforce deploy gates before merging AI-generated code
  • Detect breaking changes across multiple AI coding outputs
  • Run multi-model consensus to validate code changes

Notes

Governs AI coding across multiple assistants like Claude Code, Codex, Cursor, and Gemini CLI. Detects breaking changes, enforces deploy gates, and runs multi-model consensus checks. Available as an MCP server, a CLI tool, and a GitHub Action.

17 stars on GitHub. Last updated 2026-05-27. Licensed MIT.

Use cases

  • Enforce deploy gates before merging AI-generated code
  • Detect breaking changes across multiple AI coding outputs
  • Run multi-model consensus to validate code changes

Pros

  • Supports multiple AI coding assistants
  • Provides breaking-change detection and deploy gates
  • Delivered as versatile tools: MCP server, CLI, GitHub Action

Cons

  • Low community adoption with only 17 GitHub stars
  • May have limited documentation and support as a new project
  • Requires Python environment for local usage

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Supports multiple AI coding assistants
  • Provides breaking-change detection and deploy gates
  • Delivered as versatile tools: MCP server, CLI, GitHub Action

Cons

  • Low community adoption with only 17 GitHub stars
  • May have limited documentation and support as a new project
  • Requires Python environment for local usage