Description
Turn large volumes of feedback into reviewable patterns
Review Miner works with review data you are authorized to analyze and helps turn hundreds or thousands of texts into themes, frequencies, examples and opportunities. It preserves useful fields such as source, date, rating, product or segment and removes exact duplicates so repeated imports do not distort the result.
The agent clusters; local tools measure
The host agent performs semantic interpretation and assigns concrete themes. MCP utilities then count tags, calculate rating summaries and select representative examples while keeping original references. The result shows not only that a theme exists but how often it appears and which real reviews illustrate it.
A pattern is not automatically a product decision
Review Miner separates evidence from recommendation. A recurring complaint may relate to product, onboarding, logistics or expectations. The output proposes what to investigate and which evidence is missing instead of claiming a root cause the reviews cannot prove.


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