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MoSPI MCP server — tools & history

MoSPI MCP server: observed open status, tool schemas, linked apps and version history.

MoSPI

Endpoint: https://mcp.mospi.gov.in/

Observed status: open. Checked: 2026-10-11T06:22:49+00:00.

Linked apps

  • MoSPI

    India's official statistics via natural language

Observed tools

  • get_data — Fetches statistical data from a MoSPI dataset. This is the final step of the workflow. It requires filter values from get_metadata ΓÇö filter codes are arbitrary (e.g., indicator_code=3 means "Unemployment Rate" in PLFS but something different in other datasets). All filter parameters including limit and page go inside the filters dict, not as top-level arguments. Step 4 of: list_datasets ΓåÆ get_indicators ΓåÆ get_metadata ΓåÆ get_data
  • get_indicators — Returns the full list of available indicators for a given dataset. Datasets often have broader coverage than expected ΓÇö for example, ASI covers 57 indicators (capital structure, wages, employment, GVA, fuel consumption), and GENDER covers 154 indicators across health, education, labor, and crime. For PLFS and ASUSE, indicators are grouped by frequency_code: - PLFS frequency_code=1 (Annual): all 8 indicators including wages - PLFS frequency_code=2 (Quarterly): indicators 1-3 only - PLFS frequency_code=3 (Monthly): indicators 1-3 only frequency_code selects the indicator set, not time granularity. Step 2 of: list_datasets ΓåÆ get_indicators ΓåÆ get_metadata ΓåÆ get_data
  • get_metadata — Returns the valid filter values (states, years, quarters, etc.) for a given dataset and indicator. Filter codes are arbitrary and dataset-specific ΓÇö for example, PLFS state_code 99 means "All India", and NAS frequency_code 1 means "Annual". These values cannot be inferred or guessed from parameter names alone. The returned filter_values and api_params should be used as-is when calling get_data. Step 3 of: list_datasets ΓåÆ get_indicators ΓåÆ get_metadata ΓåÆ get_data
  • list_datasets — Returns an overview of all MoSPI statistical datasets with descriptions and coverage. This is the starting point ΓÇö call this first to identify the right dataset. The API covers 500+ indicators across employment, prices, industry, national accounts, health, education, disability, housing, environment, trade, and more. Each dataset has its own indicator codes, filter parameters, and valid values ΓÇö these are not standardized and cannot be inferred or guessed from parameter names alone. Four-step workflow (each step depends on the previous): 1. list_datasets() ΓÇö identify the dataset 2. get_indicators(dataset) ΓÇö list available indicators 3. get_metadata(dataset, indicator_code) ΓÇö retrieve valid filter values 4. get_data(dataset, filters) ΓÇö fetch the data Returns: dict with 'datasets' (name, description, use_for for each dataset) and 'workflow' (the four-step sequence).

Data as of 2026-10-11T06:22:49+00:00.

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