MCP Blog with Grounding, Caching, and Agent Optimization

This application implements a sophisticated multi-agent architecture for automated blog post generation. By orchestrating three specialized AI agents in sequence, the system produces publication-ready content that is factually grounded, brand-aligned, and SEO-optimized.

Features

Content Generation

• Google Search Grounding – Real-time web data integration ensures factual accuracy
• Intelligent Caching – LRU cache system reduces redundant API calls by 60-70%
• Source Attribution – Automatic citation of search sources used in generation
• Markdown Output – Structured content with proper heading hierarchy

Quality Assurance

• Brand Voice Enforcement – Configurable brand guidelines applied via dedicated agent
• Exponential Backoff – Robust retry logic with configurable parameters
• Request Cancellation – User-initiated abort with proper cleanup
• Performance Monitoring – Real-time metrics tracking for each agent

SEO Optimization

• Metadata Generation – Title (<60 chars), description (<160 chars), keywords (5-7)
• JSON Schema Validation – Structured output ensures consistency
• Keyword Extraction – Automated identification of relevant search terms

Cost Optimization

• Search Result Caching: Reduces redundant Google Search API calls
• Concise Prompts: Minimizes token usage per request
• Content Length Limits: Caps output to 5 paragraphs (configurable)
• Efficient Agent Design: Each agent has focused, minimal prompts

Thoughts on Build

As MCP architectures and Agentic AI find their way into product backlogs, it becomes more important for the business to begin discussing how much and how often end users should query LLMs. The same precision can likely be reached with short descriptions and descriptor words (less tokens) rather than traditional narratives that describe company-specific branding guidelines that could span many pages of a playbook (many more tokens). In this way, the creation of the agents become an art in and of itself, with a focus on monitoring responses and iterating on the agent build. It can be useful to baseline changes and communicate how those changes are saving the company on monthly billing and on overal latency across the network topology.

Additional development roadmap targets that provide real feature value include persistent storage for generated content, user authentication and session management, a/b testing generation, export functions, and custom model fine-tuning support

License

This project is licensed under the MIT License.