Building a stock-research agent? Here is a small test for conflicting signals
I'm Haiyun, founder of HPSILab. If you're building an AI workflow for stock research, here's a compact acceptance test you can adapt to any data provider.
Scenario: a model's directional signal is positive, while volatility is elevated and an options pressure level is nearby. These inputs describe different things. The agent should preserve the disagreement rather than turn them into one confident recommendation. This is an illustrative scenario, not a current market observation.
Ask your agent to produce five fields:
1. Evidence: each input, its source, and the available as-of date.
2. Coverage: which requested inputs are unavailable. Missing is not neutral.
3. Disagreement: what supports the directional view, and what challenges it.
4. Revalidation: which inputs must be fetched again before the next research session.
5. Boundary: a research summary, with no order placement or personalized buy/sell instruction.
Two failure cases to try: remove one source, then replay an older snapshot alongside a newer one. Does the agent acknowledge the gap and the date mismatch?
HPSILab supplies stock and US options research through MCP, REST and Python. To try this with our tools, create an account, connect your compatible MCP client and start with analyze_stock for a US ticker. Review the returned source status and dates; don't invent fields that aren't present.
Free accounts include trial Credits; ongoing API/MCP calls are metered. Research use only; HPSILab does not execute trades.
If you're working on this kind of agent, which failure case is hardest in your workflow? Share the client and a non-sensitive example, and I can help work through it here.
Prepared with AI assistance.
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