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jonpspri

DataBeak

by jonpspri
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

check_data_quality

Validate CSV data quality by checking completeness, consistency, duplicates, uniqueness, data types, and outliers using predefined or custom rules.

Instructions

Check data quality based on predefined or custom rules.

Returns: DataQualityResult with comprehensive quality assessment results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rulesNoList of quality rules to check (None = use default rules)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
quality_resultsYesComprehensive quality assessment results

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: +{
+  "CompletenessRule": {
+    "description": "Rule for checking data completeness.",
+    "properties": {
+      "columns": {
+        "anyOf": [
+          {
+            "items": {
+              "type": "string"
+            },
+            "type": "array"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "Specific columns to check (None for all columns)"
+      },
+      "threshold": {
+        "default": 0.95,
+        "description": "Minimum completeness ratio required (0.0-1.0)",
+        "maximum": 1,
+        "minimum": 0,
+        "type": "number"
+      },
+      "type": {
+        "const": "completeness",
+        "default": "completeness",
+        "description": "Rule type identifier",
+        "type": "string"
+      }
+    },
+    "type": "object"
+  },
+  "ConsistencyRule": {
+    "description": "Rule for checking data consistency between columns.",
+    "properties": {
+      "columns": {
+        "description": "Column pairs to check for consistency",
+        "items": {
+          "type": "string"
+        },
+        "type": "array"
+      },
+      "type": {
+        "const": "consistency",
+        "default": "consistency",
+        "description": "Rule type identifier",
+        "type": "string"
+      }
+    },
+    "type": "object"
+  },
+  "DataTypesRule": {
+    "description": "Rule for checking data type consistency.",
+    "properties": {
+      "type": {
+        "const": "data_types",
+        "default": "data_types",
+        "description": "Rule type identifier",
+        "type": "string"
+      }
+    },
+    "type": "object"
+  },
+  "DuplicatesRule": {
+    "description": "Rule for checking duplicate rows.",
+    "properties": {
+      "columns": {
+        "anyOf": [
+          {
+            "items": {
+              "type": "string"
+            },
+            "type": "array"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "default": null,
+        "description": "Columns to consider for duplicate detection (None for all columns)"
+      },
+      "threshold": {
+        "default": 0.01,
+        "description": "Maximum allowable duplicate ratio (0.0-1.0)",
+        "maximum": 1,
+        "minimum": 0,
+        "type": "number"
+      },
+      "type": {
+        "const": "duplicates",
+        "default": "duplicates",
+        "description": "Rule type identifier",
+        "type": "string"
+      }
+    },
+    "type": "object"
+  },
+  "OutliersRule": {
+    "description": "Rule for checking outliers in numeric columns.",
+    "properties": {
+      "threshold": {
+        "default": 0.05,
+        "description": "Maximum allowable outlier ratio (0.0-1.0)",
+        "maximum": 1,
+        "minimum": 0,
+        "type": "number"
+      },
+      "type": {
+        "const": "outliers",
+        "default": "outliers",
+        "description": "Rule type identifier",
+        "type": "string"
+      }
+    },
+    "type": "object"
+  },
+  "UniquenessRule": {
+    "description": "Rule for checking column uniqueness.",
+    "properties": {
+      "column": {
+        "description": "Column name to check for uniqueness",
+        "type": "string"
+      },
+      "expected_unique": {
+        "default": true,
+        "description": "Whether column values are expected to be unique",
+        "type": "boolean"
+      },
+      "type": {
+        "const": "uniqueness",
+        "default": "uniqueness",
+        "description": "Rule type identifier",
+        "type": "string"
+      }
+    },
+    "required": [
+      "column"
+    ],
+    "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?

No annotations are provided, so the description carries the full burden. It mentions 'predefined or custom rules' and returns a 'comprehensive quality assessment,' but lacks details on behavioral traits: e.g., whether it's read-only (likely, but not stated), performance implications, data sources, or error handling. The description adds minimal context beyond the basic operation.

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

Conciseness4/5

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

The description is appropriately concise with two sentences: one stating the purpose and one describing the return. It's front-loaded with the core function. No wasted words, though it could be slightly more structured (e.g., separating usage notes).

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 (multiple rule types) and rich input schema (100% coverage) with an output schema (implied by 'Returns'), the description is minimally adequate. It covers the basic purpose and return type but lacks context on when to use, behavioral details, or integration with siblings. With no annotations, it should do more to compensate.

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 fully documents the single parameter 'rules' with its types and defaults. The description adds no parameter semantics beyond what's in the schema (e.g., it doesn't explain rule interactions or provide examples). Baseline 3 is appropriate as the schema does the heavy lifting.

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: 'Check data quality based on predefined or custom rules.' It specifies the verb ('check') and resource ('data quality'), and distinguishes it from siblings like 'detect_outliers' or 'find_anomalies' by focusing on comprehensive rule-based assessment. However, it doesn't explicitly differentiate from 'validate_schema' or 'profile_data', which could be related.

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 doesn't mention when to prefer this over siblings like 'detect_outliers' (which might handle a subset of checks) or 'validate_schema' (which might focus on structural validation). No context, exclusions, or prerequisites are stated.

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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