MCP Reference
Tools19 Resources0 Templates0 Prompts0 Instructions✗ Completions✗
Data Management
- load_dataframe
-
Load a DataFrame from a CSV, Excel or Parquet file into the server’s context.
- list_available_backends
-
List available DataFrame backends (pandas, polars) installed in the environment.
- list_loaded_dataframes
-
List all DataFrames currently loaded in the server context.
- delete_dataframe
-
Remove a DataFrame from the server context to free up memory.
Data Analysis
- profile_dataframe
-
Profile a loaded DataFrame using Pointblank’s DataScan, returning column-level statistics.
Validation
- list_active_validators
-
List all validators currently active in the server context.
- delete_validator
-
Remove a validator from the server context.
- create_validator
-
Create a Pointblank Validator for a previously loaded DataFrame.
- add_validation_step
-
Add a validation step to an existing Pointblank Validator.
- apply_validation_template
-
Apply a pre-built validation template to a validator.
- get_validation_step_output
-
Retrieve output for a validation step and save it to a CSV file.
- interrogate_validator
-
Run validations and return a JSON summary with Python code equivalent.
Table Visualization
- preview_table
-
Display a preview of the DataFrame showing rows from top and bottom.
- missing_values_table
-
Generate a table showing missing values analysis for the DataFrame.
- column_summary_table
-
Generate a comprehensive column-level summary of the DataFrame.
AI & Assistant
- draft_validation_plan
-
Generate an AI-powered validation plan using Pointblank’s DraftValidation class.
- validation_assistant
-
Generate data-aware validation suggestions based on actual column types and statistics.
Reference & Management
- server_health_check
-
Get comprehensive server health and status information.
- get_pointblank_api_reference
-
Get API reference for Pointblank validation methods and common patterns.