I’ve been testing various tools over the past few months. Here’s what I’ve learned:

The Ecosystem

There’s a full open-source stack emerging for AI-assisted development:

OpenHands (MIT license)

Autonomous coding agent. I haven’t used it extensively yet, but it’s worth knowing about. It’s an open-source alternative to commercial solutions like Devin. It claims to solve 50%+ of real GitHub issues in benchmarks.

Model Context Protocol (Open Standard)

Universal interface for connecting AI agents to tools. OpenAI, Google DeepMind, and Microsoft all adopted it. This is the real game-changer: one protocol, hundreds of integrations.

MCP Servers

This is where I’ve spent most of my time:

• Context7: Real-time documentation (game-changer for preventing hallucinated APIs)
• Brave Search: Web search integration (I use this constantly)
• Filesystem, Git: Local development tools
• Playwright: Browser automation, QA testing, web scraping, E2E tests

What I’ve Actually Used

Context7 has been invaluable. When building my IAB content categorization system, having accurate, version-specific documentation fed directly to the LLM eliminated most of the “invented API” problems.

The Brave Search MCP server has been surprisingly useful for finding content on the web without leaving my development environment.

The MCP protocol itself means I can mix and match tools without being locked to one vendor’s ecosystem.

What Works Well

• Real-time documentation access (Context7) solves a huge problem with LLM coding assistants.
• With open source assistants like Kilo Code, everything can run locally if you want. No cloud dependency for sensitive work.

What Needs Work

The ecosystem is evolving rapidly, but gaps remain:

• Context Management: Even with MCP servers feeding in documentation and search results, managing what context goes to the LLM and when is best when manually curated. Especially if you want to keep your token usage under control.
• Agent orchestration, better observability, and testing frameworks are all needed.

The Emerging Opportunity

The real story is that a universal protocol (MCP) is creating a marketplace for specialized tools.

• Need better Python documentation? There’s an MCP server for that.
• Working with databases? Another server.
• Need to interact with your company’s internal APIs? You can build your own server in an afternoon.

This modularity means the ecosystem can evolve faster than any single vendor could develop features.

The Next Wave

• Specialized MCP servers for testing and QA
• Context-aware routers that select which tools to invoke
• Debugging and observability layers
• Domain-specific tool collections (data science, devops, frontend, etc.)

Or do some of these already exist and I haven’t stumbled upon them yet?

Takeaway

If you’re building AI-assisted development workflows, understanding MCP and the tool ecosystem around it is becoming essential.

What tools are you building or wish existed? What’s missing from the current ecosystem?

#AI #DevTools #SoftwareEngineering #MCP #LLM