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appcreationsca/bumpguard-mcp

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Guard your dependency bumps: an MCP server that tells your AI agent which parts of YOUR code break when you upgrade a dependency, and verifies AI-written code against the installed

A

MCP

appcreationsca/bumpguard-mcp

Added 16 June 2026

#ai #breaking-changes #dependencies #developer-tools #llm #mcp #model-context-protocol #python

Overview

Bumpguard-mcp is an MCP server that uses static analysis to detect which parts of your code will break when a dependency is upgraded. It also verifies AI-written code against the currently installed API to ensure compatibility. The tool never executes third-party code.

Best for

Best for
Developers using AI coding assistants who want to safely upgrade Python dependencies

Use cases

  • Check if upgrading a dependency introduces breaking changes in existing code
  • Validate code generated by an AI agent against the actual API of installed packages
  • Get targeted feedback on specific lines that must be changed for a dependency update

Notes

Bumpguard-mcp is an MCP server that uses static analysis to detect which parts of your code will break when a dependency is upgraded. It also verifies AI-written code against the currently installed API to ensure compatibility. The tool never executes third-party code.

0 stars on GitHub. Last updated 2026-06-16. Licensed MIT.

Use cases

  • Check if upgrading a dependency introduces breaking changes in existing code
  • Validate code generated by an AI agent against the actual API of installed packages
  • Get targeted feedback on specific lines that must be changed for a dependency update

Pros

  • Zero runtime risk because it only performs static analysis
  • Provides concrete, line-level feedback for dependency upgrades
  • Integrates with AI coding assistants via the Model Context Protocol

Cons

  • Very new project with no community adoption (0 GitHub stars)
  • Limited to Python projects only
  • Static analysis cannot catch runtime or dynamic compatibility issues

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

Pros

  • Zero runtime risk because it only performs static analysis
  • Provides concrete, line-level feedback for dependency upgrades
  • Integrates with AI coding assistants via the Model Context Protocol

Cons

  • Very new project with no community adoption (0 GitHub stars)
  • Limited to Python projects only
  • Static analysis cannot catch runtime or dynamic compatibility issues
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