dosubot[bot] commented on issue #40570:
URL: https://github.com/apache/superset/issues/40570#issuecomment-4588184442

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   Hi @eudaimos! I'm [Dosu](https://go.dosu.dev/dosubot) and I'm helping the 
Apache Superset team.
   
   This is a legitimate gap in the MCP code path. I dug into the implementation 
and can confirm the root cause:
   
   In the normal chart flow, `get_sqla_query()` explicitly merges the dataset's 
saved template parameters before creating the template processor 
[[1]](https://github.com/apache/superset/blob/829e4d92d91ceae4b43b1ed3b063ffe45377799c/superset/models/helpers.py#L1494-L1501):
   
   ```python
   template_kwargs.update(self.template_params_dict)
   template_processor = self.get_template_processor(**template_kwargs)
   ```
   
   The MCP `generate_chart` tool skips this entirely — it goes directly to 
`QueryContextFactory.create()` with the datasource and form_data but **never 
loads or merges `dataset.template_params_dict`** into the template context . 
The result is that Jinja literals are sent to the engine unrendered.
   
   Similarly, `execute_sql` will render Jinja only when `template_params` is 
explicitly passed in the request 
[[2]](https://github.com/apache/superset/pull/39858) — it doesn't consult the 
dataset's saved `template_params` on its own.
   
   This follows a pattern seen before — PR #39207 fixed a nearly identical 
issue where `template_processor` wasn't being passed through for cross-filter 
queries on virtual datasets 
[[3]](https://github.com/apache/superset/pull/39207), and PR #34802 fixed 
user-provided template parameters not being threaded through the SQL processing 
pipeline [[4]](https://github.com/apache/superset/pull/34802).
   
   **The fix** would be to have MCP tools (particularly `generate_chart` and 
the chart preview path) look up `dataset.template_params_dict` and merge it 
into the template context before creating the query context — mirroring what 
`get_sqla_query()` does at lines 1494-1501. User-provided params from the MCP 
request could override dataset defaults with a simple merge order.
   
   
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