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These MCP servers are amazing for coding

Start using MCP. NOW.

Like seriously, you’re missing out big time if you’re still not using MCP in your development workflow. It’s not just a buzzword. There’s a reason every major IDE has first-class support for it now.

So many huge productivity gains you’re just ignoring.

These are just 5 of all the incredible MCP servers that drastically improve the coding experience.

1. Sentry MCP Server

This is incredible:

Using the Sentry MCP server automatically analyze and fix an issue right from Claude:

And of course you could do this straight from Windsurf or Cursor or VS Code — letting you make major fixes to your code directly.

The Sentry MCP Server allows AI assistants to connect with Sentry, an error-tracking and performance-monitoring platform. This integration enables AI to access and analyze error data, manage projects, and monitor performance directly through the Sentry API.

Key Features:

• Error Analysis: Access and analyze Sentry issues, including error details, stack traces, and debugging information.

• Project Management: Query Sentry projects and organizations, and list or create DSNs (Data Source Names) for projects.

• AI-Powered Fixes: Use Sentry’s “Seer” to automatically analyze and suggest fixes for issues.

• Remote and Local Hosting: Sentry provides a hosted remote MCP server for easy setup, but you can also run it locally.

• Broad Compatibility: Works with various MCP clients, including Claude, Cursor, and VS Code.

2. Sequential Thinking MCP Server

Definitely one of the most important MCP servers out there anywhere.

The Sequential Thinking MCP Server is designed to help AI models break down complex problems into a series of manageable steps. It provides a structured thinking process that allows for dynamic and reflective problem-solving.

Key Features:

• Step-by-Step Problem Solving: Breaks down complex problems into a sequence of “thoughts.”

• Reflective and Dynamic: Allows for revising previous thoughts, branching into alternative lines of reasoning, and adjusting the total number of thoughts as understanding of the problem evolves.

• Structured Output: Provides a clear history of the thinking process, including branches and summaries of thoughts.

• Hypothesis Generation and Verification: Facilitates the generation and testing of potential solutions.

• Broad Applicability: Useful for planning, design, analysis, and any task where the full scope of a problem is not initially clear. It can be installed via npx or Docker and used with clients like Claude and VS Code.

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