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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Capabilities

Features and capabilities supported by this server

CapabilityDetails
tools
{
  "listChanged": true
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{
  "tasks": {
    "list": {},
    "cancel": {},
    "requests": {
      "tools": {
        "call": {}
      },
      "prompts": {
        "get": {}
      },
      "resources": {
        "read": {}
      }
    }
  }
}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_tables

List all available tables/dataframes with their descriptions.

Returns: Summary of all available tables.

show_table

Display rows from a table.

Args: name: Table name (air_quality, funding, city_info) rows: Number of rows to show (default: 10) columns: Optional list of columns to display

Returns: Formatted table data.

describe_table

Get detailed statistics for a table.

Args: name: Table name

Returns: Statistical summary and column info.

query_table

Filter a table using pandas query syntax.

Args: name: Table name query: Pandas query (e.g., "city == 'Delhi' and PM2.5 > 200")

Returns: Filtered results.

compare_weekday_weekend

Compare weekday vs weekend values for a metric.

Args: value_column: Column to compare (e.g., 'PM2.5', 'PM10') group_by: Optional grouping column (e.g., 'city') table: Table name (default: air_quality)

Returns: Comparison statistics.

compare_cities

Compare a metric across cities.

Args: value_column: Column to compare (e.g., 'PM2.5') cities: Optional list of cities to compare table: Table name (default: air_quality)

Returns: City comparison statistics.

analyze_correlation

Analyze correlations between numeric columns.

Args: columns: Optional list of columns to analyze target: Optional target column to show correlations with table: Table name (default: air_quality)

Returns: Correlation analysis.

analyze_funding

Analyze air quality funding data.

Args: city: Optional city to filter by year: Optional year to filter by

Returns: Funding analysis.

get_city_profile

Get comprehensive profile for a city including all available data.

Args: city: City name (Delhi, Bangalore, Mumbai, etc.)

Returns: City profile with air quality, funding, and metadata.

plot_comparison

Create a comparison chart.

Args: value_column: Column to plot (e.g., 'PM2.5') group_column: Grouping column (default: 'city') chart_type: 'bar', 'horizontal_bar', or 'box' table: Table name title: Optional title

Returns: Base64 encoded plot.

plot_time_series

Create a time series plot.

Args: value_column: Column to plot group_by: Optional column for separate lines (e.g., 'city') table: Table name title: Optional title

Returns: Base64 encoded plot.

plot_weekday_weekend

Create weekday vs weekend comparison chart.

Args: value_column: Column to compare group_by: Grouping column (default: 'city') table: Table name title: Optional title

Returns: Base64 encoded plot.

plot_funding_trend

Plot funding trends over years by city.

Args: cities: Optional list of cities to include title: Optional title

Returns: Base64 encoded plot.

plot_hourly_pattern

Plot hourly patterns.

Args: value_column: Column to plot group_by: Optional grouping column table: Table name title: Optional title

Returns: Base64 encoded plot.

execute_code

Execute custom Python code for advanced analysis.

Available variables:

  • air_quality, funding, city_info: DataFrames

  • pd, np, plt: Libraries

Args: code: Python code to execute

Returns: Output from code execution.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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