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jonpspri

DataBeak

by jonpspri
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Python
Remote

validate_schema

Validate data against schema definitions using Pandera framework to ensure data quality and compliance with specified rules.

Instructions

Validate data against a schema definition using Pandera validation framework.

This function leverages Pandera's comprehensive validation capabilities to provide robust data validation. The schema is dynamically converted to Pandera format and applied to the DataFrame for maximum validation coverage and reliability.

For more information on Pandera validation capabilities, see:

Returns: ValidateSchemaResult with validation status and detailed error information

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
schemaYesSchema definition with column validation rules

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
validYesWhether validation passed overall
errorsYesAll validation errors found
summaryYesSummary of validation results
validation_errorsYesValidation errors grouped by column name

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changedv1.0.0
    • addedInput schema / $defs
      Added value: +{
+  "ColumnValidationRules": {
+    "description": "Column validation rules based on Pandera Field and Check validation capabilities.\n\nThis class implements comprehensive column validation using rules compatible with\nPandera's validation system. It leverages Pandera's robust validation framework\nfor maximum data quality assurance.\n\nFor complete documentation on validation behaviors and options, see:\n- Pandera Field API: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.pandas.model_components.Field.html\n- Pandera Check API: https://pandera.readthedocs.io/en/stable/reference/generated/pandera.api.checks.Check.html\n- Pandas validation guide: https://pandas.pydata.org/docs/user_guide/basics.html#validation\n\nThe validation rules are organized by category to match Pandera's Check API for\nmaximum compatibility and comprehensive data validation coverage.",
+    "properties": {
+      "coerce": {
+        "default": false,
+        "description": "Attempt automatic type conversion (Pandera coerce parameter)",
+        "type": "boolean"
+      },
+      "equal_to": {
+        "anyOf": [
+          {
+            "type": "integer"
+          },
+          {
+            "type": "number"
+          },
+          {
+            "type": "string"
+          },
+          {
+            "type": "boolean"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "All values must equal this exact value (Pandera Check.equal_to)"
+      },
+      "greater_than": {
+        "anyOf": [
+          {
+            "type": "integer"
+          },
+          {
+            "type": "number"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "All numeric values must be strictly greater than this (Pandera Check.greater_than)"
+      },
+      "greater_than_or_equal_to": {
+        "anyOf": [
+          {
+            "type": "integer"
+          },
+          {
+            "type": "number"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "All numeric values must be >= this value (Pandera Check.greater_than_or_equal_to)"
+      },
+      "ignore_na": {
+        "default": true,
+        "description": "Ignore null values during validation checks (Pandera ignore_na parameter)",
+        "type": "boolean"
+      },
+      "in_range": {
+        "anyOf": [
+          {
+            "additionalProperties": {
+              "anyOf": [
+                {
+                  "type": "integer"
+                },
+                {
+                  "type": "number"
+                }
+              ]
+            },
+            "type": "object"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "Numeric range constraints as {'min': num, 'max': num} (Pandera Check.in_range)"
+      },
+      "isin": {
+        "anyOf": [
+          {
+            "items": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "integer"
+                },
+                {
+                  "type": "number"
+                },
+                {
+                  "type": "boolean"
+                }
+              ]
+            },
+            "type": "array"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "Values must be in this list of allowed values (Pandera Check.isin)"
+      },
+      "less_than": {
+        "anyOf": [
+          {
+            "type": "integer"
+          },
+          {
+            "type": "number"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "All numeric values must be strictly less than this (Pandera Check.less_than)"
+      },
+      "less_than_or_equal_to": {
+        "anyOf": [
+          {
+            "type": "integer"
+          },
+          {
+            "type": "number"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "All numeric values must be <= this value (Pandera Check.less_than_or_equal_to)"
+      },
+      "not_equal_to": {
+        "anyOf": [
+          {
+            "type": "integer"
+          },
+          {
+            "type": "number"
+          },
+          {
+            "type": "string"
+          },
+          {
+            "type": "boolean"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "No values may equal this value (Pandera Check.not_equal_to)"
+      },
+      "notin": {
+        "anyOf": [
+          {
+            "items": {
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "integer"
+                },
+                {
+                  "type": "number"
+                },
+                {
+                  "type": "boolean"
+                }
+              ]
+            },
+            "type": "array"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "Values must not be in this list of forbidden values (Pandera Check.notin)"
+      },
+      "nullable": {
+        "default": true,
+        "description": "Allow null/NaN values in the column (Pandera nullable parameter)",
+        "type": "boolean"
+      },
+      "raise_warning": {
+        "default": false,
+        "description": "Raise warning instead of exception on validation failure (Pandera raise_warning parameter)",
+        "type": "boolean"
+      },
+      "str_contains": {
+        "anyOf": [
+          {
+            "type": "string"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "Strings must contain this substring (Pandera Check.str_contains)"
+      },
+      "str_endswith": {
+        "anyOf": [
+          {
+            "type": "string"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "Strings must end with this suffix (Pandera Check.str_endswith)"
+      },
+      "str_length": {
+        "anyOf": [
+          {
+            "additionalProperties": {
+              "type": "integer"
+            },
+            "type": "object"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "String length constraints as {'min': int, 'max': int} (Pandera Check.str_length)"
+      },
+      "str_matches": {
+        "anyOf": [
+          {
+            "type": "string"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "Strings must match this regex pattern (Pandera Check.str_matches)"
+      },
+      "str_startswith": {
+        "anyOf": [
+          {
+            "type": "string"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "Strings must start with this prefix (Pandera Check.str_startswith)"
+      },
+      "unique": {
+        "default": false,
+        "description": "Ensure all column values are unique (Pandera unique parameter)",
+        "type": "boolean"
+      }
+    },
+    "type": "object"
+  },
+  "ValidationSchema": {
+    "additionalProperties": {
+      "$ref": "#/$defs/ColumnValidationRules"
+    },
+    "description": "Schema definition for data validation.",
+    "type": "object"
+  }
+}
  2. First observed

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions that the tool 'leverages Pandera's comprehensive validation capabilities' and returns 'validation status and detailed error information', but doesn't specify important behavioral aspects: whether this is a read-only operation, what happens on validation failure (exceptions vs. warnings), performance characteristics, or data size limitations. The description adds some context about Pandera framework but misses critical operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately concise but includes unnecessary promotional language ('comprehensive validation capabilities', 'maximum validation coverage and reliability') and external documentation links that don't help the AI agent. The core purpose is stated upfront, but the second paragraph and documentation links add bulk without operational value. The 'Returns:' section is useful but could be more integrated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (data validation framework integration), no annotations, and the presence of an output schema, the description is minimally adequate. It identifies the framework and return type but misses important context: what data format is expected (presumably pandas DataFrame based on Pandera reference), how data is provided to the tool (not mentioned in parameters), error handling behavior, and performance considerations. The output schema existence reduces but doesn't eliminate the need for more operational context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents the single 'schema' parameter with extensive validation rule details. The description doesn't add any parameter-specific information beyond what's in the schema - it doesn't explain how to structure the schema parameter, provide examples, or clarify the relationship between the schema parameter and the data being validated. Baseline 3 is appropriate when schema does all the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Validate data against a schema definition using Pandera validation framework.' It specifies the verb (validate), resource (data), and framework (Pandera). However, it doesn't explicitly distinguish this from sibling tools like 'check_data_quality' or 'profile_data', which might have overlapping functionality in data validation contexts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions Pandera's capabilities but doesn't specify scenarios where this validation tool is appropriate compared to sibling tools like 'check_data_quality' or 'profile_data'. There's no mention of prerequisites, data format requirements, or when-not-to-use conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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