Data

MoSPI

by NSO India

Overview

Access India's official government statistics through natural language. MoSPI's MCP server connects AI assistants to national datasets covering GDP, inflation, employment, industrial production, higher education, health, energy, environment, trade, and more. All data comes directly from the Ministry of Statistics and Programme Implementation (MoSPI), Government of India, via the eSankhyiki portal.

Tools

get_data

Claude

Fetches statistical data from a MoSPI dataset. This is the final step of the workflow. It requires filter values from get_metadata — 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 → get_indicators → get_metadata → get_data

get_indicators

Claude

Returns the full list of available indicators for a given dataset. Datasets often have broader coverage than expected — for example, ASI covers 57 indicators (capital structure, wages, employment, GVA, fuel consumption), and GENDER covers 147 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 → get_indicators → get_metadata → get_data

get_metadata

Claude

Returns the valid filter values (states, years, quarters, etc.) for a given dataset and indicator. Filter codes are arbitrary and dataset-specific — 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 → get_indicators → get_metadata → get_data

list_datasets

Claude

Returns an overview of all MoSPI statistical datasets with descriptions and coverage. This is the starting point — 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 — 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() — identify the dataset 2. get_indicators(dataset) — list available indicators 3. get_metadata(dataset, indicator_code) — retrieve valid filter values 4. get_data(dataset, filters) — fetch the data Returns: dict with 'datasets' (name, description, use_for for each dataset) and 'workflow' (the four-step sequence).

step1_know_about_mospi_api

Claude

step2_get_indicators

Claude

step3_get_metadata

Claude

step4_get_data

Claude

App Stats

Tools

Prompts

Apr 21, 2026

First seen

ChatGPT, Claude

Platforms