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- These 5 MCP servers changed how I code forever
These 5 MCP servers changed how I code forever
Supercharge your IDE with these.
Sanket Gawas
July 29, 2025 • Estimated Reading Time: 8 minutes
We all know how MCP servers are transforming our development workflows. I came across some really amazing ones this week that I think you guys shouldn’t miss out on and give it a try.
1. Context7 MCP
Context7 transforms how AI assistants access external library documentation. Rather than relying on potentially outdated training data, the server dynamically retrieves current documentation and code examples directly from official sources.
When you reference a specific library or function, Context7 automatically identifies the context, retrieves relevant documentation sections and injects precise information into your AI assistant's working memory.
Why I like it
Dynamically serves real-time, version-specific API docs.
Summarizes usage, methods, and examples from trusted sources.
Supports 20,000+ libraries and frameworks, updated continuously.
2. REF MCP - Context7 but better
Solves the same problem like context7 but with less token utilization.
REF MCP solves this through intelligent documentation streaming. Rather than processing entire API references, the server delivers precisely the information needed for specific tasks.
Why I like it
85% reduction in token usage compared to Context7
Real-time docs from public and private sources
Integration with private repositories and internal docs
Practical example: REF grabs the exact section from OpenAI's latest API docs, skipping the 90% of documentation that's irrelevant to your task.
3. Semgrep MCP - Security analysis
Security analysis in AI-assisted development presents unique challenges. Most coding assistants analyze individual files, missing architectural vulnerabilities that span entire systems.
Semgrep MCP addresses this limitation by providing AI agents with comprehensive security scanning capabilities. The integration brings 2,000+ security rules directly into development workflows.
Why I like it
OWASP compliance validation, secret detection and remediation and custom policy enforcement.
Understands the full project structure, not just file level patterns.
Returns issues with reasons and remediation tips.
Can run via HTTP, Docker or local CLI without needing cloud upload.
4. Pieces – A developer memory graph
Perhaps the most innovative MCP server that addresses a fundamental limitation of current AI development tools, the inability to learn from past problem solving sessions.
Pieces MCP creates structured memory graphs of development activities. Terminal commands, debugging sessions, successful solutions and dead ends become searchable knowledge that AI agents can reference and build upon.
Why I like it
Remembers how you solved something.
Captures developer activity across terminal, editor, browser and issue trackers.
Timeline based debugging session capture.
Privacy focused local first storage.
Graph view of past investigations apart from flat logs.
5. Playwright – Autonomous UI review and QA
Frontend development presents unique challenges for AI assistance due to the visual and interactive nature of user interfaces. Playwright MCP bridges this gap by enabling AI agents to see, test and analyze user interfaces directly.
The server captures UI snapshots, runs accessibility audits and connects visual issues to specific code locations.
Why I like it
Captures live UI snapshots from dev, PRs or CI build.
Sends screenshots to Claude (or others) for QA and debugging.
Can run continuously and self-improve on each commit.
Cross-browser compatibility testing.
👀 Whats shipping this week?
GitHub Spark - Microsoft's new app generation tool serving as an entry point to GitHub Copilot experience, targeting less technical users with a path to full IDE functionality for application expansion.
Gemini 2.5 Flash Lite generally available - Google's ultra fast, cheap model can process millions of words for under $1/month.
grok-cli - Command-line interface tool for Grok models, expanding terminal-based AI development options beyond Claude Code and Gemini CLI
OpenAI GPT-5 launching in August - OpenAI plans to release GPT-5 as a unified AI system incorporating distinct models for different functions, including the o3 reasoning model, merging o-series and GPT-series capabilities.
Kiro agentic IDE - New agentic IDE that works alongside developers from prototype to production, representing the next evolution of AI-powered development environments.
Cursor Bugbot for Automated Code Review - Cursor's new safety net automatically catches bugs and security vulnerabilities in pull requests before production, with early users reporting over 50% resolution rates.
Kimi K2 vs Claude 4 Sonnet benchmark for agentic coding - Moonshot AI's low-cost open-source Kimi K2 model showed competitive coding performance against Claude 4 Sonnet with significant cost advantages for automated development workflows.
📖 Worth the scroll
Rethinking CLI interfaces for AI - Exploration of how command line interfaces need to evolve to better accommodate AI powered development workflows and agent interactions.
Startup AI budget allocation guidance - Startups should allocate 10-15% of R&D budget to AI tools (about $30k annually vs $200k engineer salaries), with AI-native companies potentially spending more as integration deepens.
🤓 Case Studies
How Anthropic teams use Claude Code - Internal case studies from Anthropic's data infrastructure, product development, security engineering, and growth marketing teams showing real-world Claude Code adoption across technical and non-technical roles.
Anthropic's Automated Auditing Research - Anthropic's alignment team explores automated methods for auditing AI systems, providing frameworks for developers building AI safety and monitoring into their applications.
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💬 Quick question: What's the most time consuming part of your development workflow? Reply and I’ll build a tutorial to automate it.
Thanks for reading
- Sanket