I built a financial analyzer agent with MCP Agent that pulls stock-related data from the web, verifies the quality of the information, analyzes it, and generates a structured markdown report. (My partner needed one, so I built it to help him make better decisions lol.) It’s fully automated and runs locally using MCP servers for fetching data, evaluating quality, and writing output to disk.
At first, the results weren’t great. The data was inconsistent, and the reports felt shallow. So I added an EvaluatorOptimizer, a function that loops between the research agent and an evaluator until the output hits a high-quality threshold. That one change made a huge difference.
In my opinion, the real strength of this setup is the orchestrator. It controls the entire flow: when to fetch more data, when to re-run evaluations, and how to pass clean input to the analysis and reporting agents. Without it, coordinating everything would’ve been a mess. Also, it’s always fun watching the logs and seeing how the LLM thinks!
Take a look and let me know what you think.
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