VayuChat MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| 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
| Name | Description |
|---|---|
| list_tablesB | List all available tables/dataframes with their descriptions. Returns: Summary of all available tables. |
| show_tableC | 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_tableC | Get detailed statistics for a table. Args: name: Table name Returns: Statistical summary and column info. |
| query_tableB | 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_weekendC | 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_citiesC | 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_correlationC | 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_fundingC | Analyze air quality funding data. Args: city: Optional city to filter by year: Optional year to filter by Returns: Funding analysis. |
| get_city_profileB | 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_comparisonC | 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_seriesC | 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_weekendC | 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_trendB | Plot funding trends over years by city. Args: cities: Optional list of cities to include title: Optional title Returns: Base64 encoded plot. |
| plot_hourly_patternC | 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_codeC | Execute custom Python code for advanced analysis. Available variables:
Args: code: Python code to execute Returns: Output from code execution. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 15 tools
Most tools have distinct purposes focused on air quality data analysis, but some overlap exists between plot tools (e.g., plot_time_series and plot_hourly_pattern could be confused for time-based visualizations) and between describe_table and show_table for data inspection. The descriptions help differentiate them, but careful reading is needed.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., analyze_correlation, compare_cities, plot_time_series). The naming is predictable and readable throughout the set, with no deviations in style.
15 tools are well-scoped for an air quality data analysis server, covering data querying, statistical analysis, visualization, and custom code execution. Each tool earns its place without feeling excessive or insufficient for the domain.
The toolset provides comprehensive coverage for air quality analysis, including data inspection, filtering, statistical comparisons, and various visualizations. A minor gap is the lack of tools for data modification (e.g., update or delete operations), but this is reasonable for an analysis-focused server, and agents can work around it with execute_code if needed.