Why You’ll Never Build AI Agents The Old Way Again
AI Summary
Importance of MCPS
- MCPS are being adopted as the new standard in the AI industry.
- They allow AI agents to connect to third-party tools, enhancing collaboration among developers.
Key Features of MCPS
- Discoverable at Runtime: Unlike Open API, MCPS can dynamically update without manual intervention.
- Mapping to AI Agent Components:
- Tools: Correspond to actions agents perform.
- Resources: Represent knowledge needed by agents.
- Prompts: Handle instructions from users.
- Ease of Running Locally: MCP servers can be easily hosted, streamlining development and testing.
Implications for Developers
- Upcoming SAS platforms will require MCP servers for AI agents, simplifying integration with third-party platforms.
- Developers can create and share custom tools that combine multiple platforms, opening monetization opportunities.
Recommended MCP Servers
- Zapier MCP: Connects agents to 6,000+ apps.
- Browser Base: Automates complex browsing tasks.
- Fire Crawl: Scrapes websites for LLM-ready data.
- Figma Context: Helps agents understand design layouts.
- GPT Researcher: Enables deep and structured research.
- E2B: Executes Python and JavaScript code in remote environments.
Building AI Agents with MCPS
- Use platforms like Cursor for developing AI agents without coding.
- Create a GitHub repository for the agency’s files and MCP documentation.
- Generate and test the agent using Cursor, which automates several tasks, including fetching YouTube comments and generating insights for content creation.