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Introduces a dynamic filter layer in the chart type registry so operators can disable individual plugins (e.g. `handlebars`) without a code deploy: - `MCP_DISABLED_CHART_PLUGINS: frozenset[str]` — static deny-list in mcp_config.py - `MCP_CHART_PLUGIN_ENABLED_FUNC: Callable[[str], bool] | None` — dynamic hook for Harness/Split/per-user targeting; takes precedence over the deny-list - Both keys are propagated through `get_mcp_config()` defaults registry.py changes: - `_PluginFilterConfig` frozen dataclass replaces two bare globals so configure() replaces them atomically (no torn reads under concurrency) - `configure(disabled, enabled_func)` — called at app init; accepts any iterable for `disabled`; validates `enabled_func` is callable - `_is_plugin_enabled()` — reads config once, fails closed on callable exception - `get()` / `all_types()` / `is_enabled()` apply the filter at lookup time; `is_registered()` and `display_name_for_viz_type()` intentionally bypass it so callers can distinguish "unknown" vs "disabled" and existing charts still resolve display names for disabled viz types schema_validator.py: two-step pre-check — `is_registered()` for unknown types, `is_enabled()` for disabled ones, with distinct `DISABLED_CHART_TYPE` error code. Wiring: - `SupersetAppInitializer.configure_mcp_chart_registry()` called after `configure_feature_flags()` in `init_app()` - `flask_singleton.py` re-calls `registry.configure()` after the MCP config overlay so MCP-specific overrides in `superset_config.py` take effect in standalone MCP mode Tests: 28 cases in test_registry_filters.py covering deny-list, callable hook, fail-closed on exception, all_types() filtering, display_name bypass, atomic reconfigure, and configure() with list/tuple/frozenset inputs. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
244 lines
10 KiB
Python
Executable File
244 lines
10 KiB
Python
Executable File
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""
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Unified schema validation for chart configurations.
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Consolidates pre-validation, schema validation, and error enhancement.
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"""
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import logging
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from typing import Any, Dict, Tuple
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from pydantic import ValidationError as PydanticValidationError
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from superset.mcp_service.chart.schemas import (
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GenerateChartRequest,
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)
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from superset.mcp_service.common.error_schemas import ChartGenerationError
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logger = logging.getLogger(__name__)
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class SchemaValidator:
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"""Unified schema validator with pre-validation and enhanced error messages."""
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@staticmethod
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def validate_request(
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request_data: Dict[str, Any],
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) -> Tuple[bool, GenerateChartRequest | None, ChartGenerationError | None]:
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"""
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Validate request data with pre-validation and enhanced error handling.
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Returns:
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Tuple of (is_valid, parsed_request, error)
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"""
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# Pre-validate to catch common issues early
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is_valid, error = SchemaValidator._pre_validate(request_data)
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if not is_valid:
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return False, None, error
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# Try Pydantic validation
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try:
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request = GenerateChartRequest(**request_data)
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return True, request, None
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except PydanticValidationError as e:
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# Enhance the error message
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error = SchemaValidator._enhance_validation_error(e, request_data)
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return False, None, error
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@staticmethod
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def _pre_validate(
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data: Dict[str, Any],
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) -> Tuple[bool, ChartGenerationError | None]:
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"""Pre-validate request data before Pydantic processing."""
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if not isinstance(data, dict):
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return False, ChartGenerationError(
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error_type="invalid_request_format",
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message="Request must be a JSON object",
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details="The request body must be a valid JSON object, not a string "
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"or array",
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suggestions=[
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"Ensure you're sending a JSON object with 'dataset_id' and "
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"'config' fields",
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"Check that Content-Type header is set to 'application/json'",
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],
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error_code="INVALID_REQUEST_FORMAT",
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)
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# Check for required top-level fields
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if "dataset_id" not in data:
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return False, ChartGenerationError(
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error_type="missing_dataset_id",
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message="Missing required field: dataset_id",
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details="The 'dataset_id' field is required to identify which dataset "
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"to use",
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suggestions=[
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"Add 'dataset_id' field with the ID of your dataset",
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"Use list_datasets tool to find available dataset IDs",
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"Example: {'dataset_id': 1, 'config': {...}}",
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],
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error_code="MISSING_DATASET_ID",
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)
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if "config" not in data:
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return False, ChartGenerationError(
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error_type="missing_config",
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message="Missing required field: config",
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details="The 'config' field is required to specify chart configuration",
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suggestions=[
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"Add 'config' field with chart type and settings",
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"Example: {'dataset_id': 1, 'config': {'chart_type': 'xy', ...}}",
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],
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error_code="MISSING_CONFIG",
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)
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config = data.get("config", {})
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if not isinstance(config, dict):
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return False, ChartGenerationError(
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error_type="invalid_config_format",
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message="Config must be a JSON object",
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details="The 'config' field must be a valid JSON object with chart "
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"settings",
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suggestions=[
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"Ensure config is an object, not a string or array",
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"Example: 'config': {'chart_type': 'xy', 'x': {...}, 'y': [...]}",
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],
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error_code="INVALID_CONFIG_FORMAT",
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)
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# Check chart_type early
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chart_type = config.get("chart_type")
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if not chart_type:
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return False, ChartGenerationError(
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error_type="missing_chart_type",
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message="Missing required field: chart_type",
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details="Chart configuration must specify 'chart_type'",
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suggestions=[
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"Add 'chart_type': 'xy' for line/bar/area/scatter charts",
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"Add 'chart_type': 'table' for table visualizations",
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"Add 'chart_type': 'pie' for pie or donut charts",
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"Add 'chart_type': 'pivot_table' for interactive pivot tables",
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"Add 'chart_type': 'mixed_timeseries' for dual-series time charts",
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"Add 'chart_type': 'handlebars' for custom HTML template charts",
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"Add 'chart_type': 'big_number' for big number display",
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"Example: 'config': {'chart_type': 'xy', ...}",
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],
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error_code="MISSING_CHART_TYPE",
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)
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return SchemaValidator._pre_validate_chart_type(chart_type, config)
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@staticmethod
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def _pre_validate_chart_type(
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chart_type: str,
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config: Dict[str, Any],
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) -> Tuple[bool, ChartGenerationError | None]:
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"""Validate chart type and dispatch to plugin pre-validation."""
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from superset.mcp_service.chart.registry import get_registry
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registry = get_registry()
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if not isinstance(chart_type, str) or not registry.is_registered(chart_type):
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valid_types = ", ".join(registry.all_types())
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return False, ChartGenerationError(
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error_type="invalid_chart_type",
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message=f"Invalid chart_type: '{chart_type}'",
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details=f"Chart type '{chart_type}' is not supported. "
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f"Must be one of: {valid_types}",
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suggestions=[
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"Use 'chart_type': 'xy' for line, bar, area, or scatter charts",
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"Use 'chart_type': 'table' for tabular data display",
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"Use 'chart_type': 'pie' for pie or donut charts",
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"Use 'chart_type': 'pivot_table' for interactive pivot tables",
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"Use 'chart_type': 'mixed_timeseries' for dual-series time charts",
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"Use 'chart_type': 'handlebars' for custom HTML template charts",
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"Use 'chart_type': 'big_number' for big number display",
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"Check spelling and ensure lowercase",
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],
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error_code="INVALID_CHART_TYPE",
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)
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if not registry.is_enabled(chart_type):
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valid_types = ", ".join(registry.all_types())
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return False, ChartGenerationError(
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error_type="disabled_chart_type",
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message=f"Chart type '{chart_type}' is not enabled on this instance",
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details=f"Chart type '{chart_type}' is registered but has been "
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f"disabled by the operator. "
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f"Enabled chart types: {valid_types}",
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suggestions=[
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f"Use one of the enabled chart types: {valid_types}",
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"Contact your administrator if you believe this is an error",
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],
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error_code="DISABLED_CHART_TYPE",
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)
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plugin = registry.get(chart_type)
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if plugin is None:
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return False, ChartGenerationError(
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error_type="invalid_chart_type",
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message=f"Chart type '{chart_type}' has no registered plugin",
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details="Internal error: chart type is listed but has no plugin",
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suggestions=["Use a supported chart_type"],
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error_code="INVALID_CHART_TYPE",
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)
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if (error := plugin.pre_validate(config)) is not None:
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return False, error
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return True, None
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@staticmethod
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def _enhance_validation_error(
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error: PydanticValidationError, request_data: Dict[str, Any]
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) -> ChartGenerationError:
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"""Convert Pydantic validation errors to user-friendly messages."""
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errors = error.errors()
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# Check for discriminated union errors (generic "'table' was expected")
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for err in errors:
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if err.get("type") == "union_tag_invalid" or "discriminator" in str(
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err.get("ctx", {})
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):
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from superset.mcp_service.chart.registry import get_registry
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chart_type = request_data.get("config", {}).get("chart_type", "")
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plugin = get_registry().get(chart_type)
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if plugin is not None:
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hint = plugin.schema_error_hint()
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if hint is not None:
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return hint
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# Default enhanced error
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error_details = []
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for err in errors[:3]: # Show first 3 errors
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loc = " -> ".join(str(location) for location in err.get("loc", []))
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msg = err.get("msg", "Validation failed")
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error_details.append(f"{loc}: {msg}" if loc else msg)
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return ChartGenerationError(
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error_type="validation_error",
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message="Chart configuration validation failed",
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details="; ".join(error_details) or "Invalid chart configuration structure",
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suggestions=[
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"Check that all required fields are present",
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"Ensure field types match the schema",
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"Use get_dataset_info to verify column names",
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"Refer to the API documentation for field requirements",
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],
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error_code="VALIDATION_ERROR",
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)
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