Solutions for Improving the Accuracy of Natural Language Queries
The following measures can be taken when there is a semantic parsing bias in the MCP in conjunction with the big model:
- Structured Instruction Templates: Organize the query in a fixed format of 'timeframe + analysis dimensions + core metrics + filtering criteria', e.g. 'show the number of active users grouped by device type over the past 7 days, excluding internal traffic'.
- Field Alias Mapping: Predefine common aliases for dimensions/metrics in settings.json, e.g. map 'number of users' to 'activeUsers', 'accessSource ' maps to 'sessionSource'.
- step by step verification: First confirm the list of fields supported by the target attribute with get_dimensions and get_metrics, then construct the query statement with these standard fields.
- error fallback mechanism: When a complex query fails, it can be split into multiple simple queries - e.g., get the country distribution first, then query each country's behavioral data separately.
Special recommendation: for business critical scenarios, a small amount of test data should be used to validate query accuracy before expanding to formal analysis. models such as Claude/Cursor support few-shot learning, which can provide 3-5 correct query examples to improve parsing accuracy.
This answer comes from the articleGoogle Analytics MCP: A Local Server Tool for Connecting GA4 Data to Big ModelsThe
May not be reproduced without permission:AI productivity tools " How to overcome the problem of inaccurate semantic parsing when querying GA4 data in natural language?
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