inspect_data_around
Analyze data patterns and relationships around specific coordinates for contextual understanding, data validation, and error investigation in CSV datasets.
Instructions
Inspect data around a specific coordinate for contextual analysis.
Examines the data surrounding a specific cell to understand context, patterns, and relationships. Useful for data validation, error investigation, and understanding local data patterns.
Returns: Contextual view of data around the specified coordinates
Inspection Features: 📍 Center Point: Specified cell as reference point 🔍 Radius View: Configurable area around center cell 📊 Data Context: Surrounding values for pattern analysis 🎯 Coordinates: Clear row/column reference system
Examples: # Inspect around a specific data point context = await inspect_data_around(ctx, row=50, column_name="price", radius=3)
# Minimal context view
context = await inspect_data_around(ctx, row=10,
column_name="status", radius=1)
AI Workflow Integration: 1. Error investigation and data quality assessment 2. Pattern recognition in local data areas 3. Understanding data relationships and context 4. Validation of data transformations and corrections
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| row | Yes | Row index to center the inspection (0-based) | |
| column_name | Yes | Name of the column to center on | |
| radius | No | Number of rows/columns to include around center point |
Implementation Reference
- The async handler function that executes the tool's core logic: retrieves the dataframe from session, validates the column, calculates bounds around the specified row and column based on radius, slices the data, formats records with proper type handling, creates a DataPreview of surrounding data, and returns InspectDataResult.async def inspect_data_around( ctx: Annotated[Context, Field(description="FastMCP context for session access")], row: Annotated[int, Field(description="Row index to center the inspection (0-based)")], column_name: Annotated[str, Field(description="Name of the column to center on")], radius: Annotated[ int, Field(description="Number of rows/columns to include around center point"), ] = 2, ) -> InspectDataResult: """Inspect data around a specific coordinate for contextual analysis. Examines the data surrounding a specific cell to understand context, patterns, and relationships. Useful for data validation, error investigation, and understanding local data patterns. Returns: Contextual view of data around the specified coordinates Inspection Features: 📍 Center Point: Specified cell as reference point 🔍 Radius View: Configurable area around center cell 📊 Data Context: Surrounding values for pattern analysis 🎯 Coordinates: Clear row/column reference system Examples: # Inspect around a specific data point context = await inspect_data_around(ctx, row=50, column_name="price", radius=3) # Minimal context view context = await inspect_data_around(ctx, row=10, column_name="status", radius=1) AI Workflow Integration: 1. Error investigation and data quality assessment 2. Pattern recognition in local data areas 3. Understanding data relationships and context 4. Validation of data transformations and corrections """ # Get session_id from FastMCP context session_id = ctx.session_id _session, df = get_session_data(session_id) # Handle column specification column = column_name if isinstance(column, int): if column < 0 or column >= len(df.columns): raise InvalidParameterError( "column_name", # noqa: EM101 column, f"integer between 0 and {len(df.columns) - 1}", ) column_name = df.columns[column] col_index = column else: if column not in df.columns: raise ColumnNotFoundError(column, df.columns.tolist()) column_name = column col_index_result = df.columns.get_loc(column) col_index = col_index_result if isinstance(col_index_result, int) else 0 # Calculate bounds row_start = max(0, row - radius) row_end = min(len(df), row + radius + 1) col_start = max(0, col_index - radius) col_end = min(len(df.columns), col_index + radius + 1) # Get column slice cols_slice = df.columns[col_start:col_end].tolist() # Get data slice data_slice = df.iloc[row_start:row_end][cols_slice] # Convert to records with row indices records = [] for _, (orig_idx, row_data) in enumerate(data_slice.iterrows()): # Handle different index types from iterrows safely row_index_val = int(orig_idx) if isinstance(orig_idx, int) else 0 record: dict[str, CsvCellValue] = {"__row_index__": row_index_val} record.update(row_data.to_dict()) # Handle pandas/numpy types for key, value in record.items(): if key == "__row_index__": continue if pd.isna(value): record[key] = None elif isinstance(value, pd.Timestamp): record[key] = str(value) elif hasattr(value, "item"): record[key] = value.item() records.append(record) # Create DataPreview from the records surrounding_data = DataPreview( rows=records, row_count=len(records), column_count=len(cols_slice), truncated=False, ) return InspectDataResult( center_coordinates={"row": row, "column": column_name}, surrounding_data=surrounding_data, radius=radius, )
- src/databeak/servers/discovery_server.py:856-856 (registration)Registers the inspect_data_around handler as a FastMCP tool with the exact name 'inspect_data_around' on the discovery_server instance.discovery_server.tool(name="inspect_data_around")(inspect_data_around)
- Pydantic model defining the output schema for the inspect_data_around tool response, including center coordinates, surrounding data preview, and radius used.class InspectDataResult(BaseToolResponse): """Response model for contextual data inspection.""" center_coordinates: dict[str, Any] surrounding_data: DataPreview radius: int
- Function signature with Annotated parameters providing input schema validation and descriptions for FastMCP tool schema generation: ctx (Context), row (int), column_name (str), radius (int=2). Returns InspectDataResult.async def inspect_data_around( ctx: Annotated[Context, Field(description="FastMCP context for session access")], row: Annotated[int, Field(description="Row index to center the inspection (0-based)")], column_name: Annotated[str, Field(description="Name of the column to center on")], radius: Annotated[ int, Field(description="Number of rows/columns to include around center point"), ] = 2, ) -> InspectDataResult: