How do I use MoSPI MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MoSPI MCP Server What is the latest unemployment rate in India?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MoSPI MCP Server

Official

by nso-india

Overview Schema Related Servers Score Discussions

Python

Hybrid

MoSPI MCP Server

License: MIT Python 3.11+ FastMCP MCP Server Tests

MCP (Model Context Protocol) server for accessing India's Ministry of Statistics and Programme Implementation (MoSPI) data APIs. Built with FastMCP 3.3.

Related MCP server: datagovin-mcp-server

Overview

This server provides AI-ready access to official Indian government statistics through the Model Context Protocol (MCP). It acts as a bridge between AI assistants (Claude, ChatGPT, Cursor, etc.) and MoSPI's open data APIs, enabling natural language queries for economic, demographic, and social indicators.

Key Features:

25 statistical datasets covering employment, inflation, industrial production, GDP, energy, renewable energy, higher education, school education, gender, health, disability, housing, NSS education consumption, environment, trade, agriculture, consumption, economic census, and digital literacy
Sequential 4-tool workflow designed for LLM consumption
Swagger-driven parameter validation
Full OpenTelemetry integration for observability
Production-ready Docker deployment

Datasets

Dataset	Full Name	Use For
PLFS	Periodic Labour Force Survey	Jobs, unemployment, wages, workforce participation
CPI	Consumer Price Index	Retail inflation, cost of living, commodity prices
IIP	Index of Industrial Production	Industrial growth, manufacturing output
ASI	Annual Survey of Industries	Factory performance, industrial employment
NAS	National Accounts Statistics	GDP, economic growth, national income
WPI	Wholesale Price Index	Wholesale inflation, producer prices
ENERGY	Energy Statistics	Energy production, consumption, fuel mix
AISHE	All India Survey on Higher Education	Universities, colleges, student enrolment, GER, GPI
ASUSE	Annual Survey of Unincorporated Enterprises	Informal sector, small businesses, MSME statistics
GENDER	Gender Statistics	Gender indicators, women empowerment, sex ratio, crimes against women
NFHS	National Family Health Survey	Fertility, infant mortality, maternal care, nutrition
ENVSTATS	Environment Statistics	Climate, biodiversity, pollution, water resources, forests
RBI	RBI Statistics	Foreign trade, forex reserves, exchange rates, balance of payments
NSS77	NSS 77th Round (Land & Livestock + AIDIS)	Agricultural households, land & livestock, farm income; household assets, debt & borrowing (module=land_livestock or aidis)
NSS78	NSS 78th Round (Living Conditions)	Drinking water, sanitation, digital connectivity, migration
CPIALRL	CPI for Agricultural/Rural Labourers	Rural inflation, agricultural labourer cost of living
HCES	Household Consumption Expenditure Survey	Consumer spending, poverty analysis, inequality (Gini)
TUS	Time Use Survey	Time allocation, unpaid work, gender time gaps
EC	Economic Census	Establishments, enterprises, district-wise business count, workers
NSS79	NSS 79th Round (CAMS + AYUSH)	Literacy, school enrolment, NEET youth, health expenditure, financial inclusion, digital skills, AYUSH awareness and usage
UDISE	UDISE+ (Unified District Information System for Education)	Schools, enrolment, dropout rates, teachers, PTR, GER, NER, GPI, CWSN, school infrastructure, ICT labs, minority enrolment
MNRE	Renewable Energy (Ministry of New and Renewable Energy)	State-wise monthly installed capacity (MW) for solar, wind, hydro, bio, and total renewable power
NSS76	NSS 76th Round (Disability + Housing & Drinking Water)	Disability prevalence, literacy and education among PwD, care arrangements, aid/help, drinking water sources, water treatment, housing characteristics, latrines, flood experience
NSS75E	NSS 75th Round (Education / Social Consumption on Education)	Literacy rate, educational attainment, GAR/NAR, student attendance, course-type distribution, education expenditure, household computer and internet access
NSS80	NSS 80th Round (Telecom (CMST) + Education (CMSE))	Mobile phone ownership, internet usage, online banking, cybercrime, household telecom connectivity, school enrolment and expenditure course fees, private coaching

MCP Tools

The server exposes 4 tools that follow a sequential workflow:

list_datasets  ΓåÆ  get_indicators  ΓåÆ  get_metadata  ΓåÆ  get_data

Step	Tool	Description
1	`list_datasets()`	Overview of all datasets. Start here to find the right dataset.
2	`get_indicators(dataset)`	List available indicators for the chosen dataset.
3	`get_metadata(dataset, ...)`	Get valid filter values (states, years, categories) and API parameters.
4	`get_data(dataset, filters)`	Fetch data using filter key-value pairs from metadata.

Important: Tools must be called in order. Skipping get_metadata will result in invalid filter codes.

Quick Start

If you want to connect your AI agent of choice with the MCP server, you can directly connect it with MOSPI's MCP server. Video Guides to connect ChatGPT or Claude to MCP are available here -

https://github.com/user-attachments/assets/ec23db03-c5ad-4bdd-af3a-9387bd906b3c

https://github.com/user-attachments/assets/4d2adb2a-a350-4563-8408-c0790bb94412

To get more information, visit - https://www.datainnovation.mospi.gov.in/mospi-mcp

The instructions below are for self-hosting the MCP server.

Installation

# Clone the repository
git clone https://github.com/nso-india/esankhyiki-mcp.git
cd esankhyiki-mcp

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running the Server

# HTTP transport (remote access)
python mospi_server.py

# OR using FastMCP CLI
fastmcp run mospi_server.py:mcp --transport http --port 8000

# stdio transport (local MCP clients)
fastmcp run mospi_server.py:mcp

Server runs at http://localhost:8000/mcp

Connecting from CLI Tools

Server URL: https://mcp.mospi.gov.in/

Claude Code

claude mcp add esankhyiki-mcp --transport http https://mcp.mospi.gov.in/

Verify with claude mcp list.

Cursor / Windsurf

Add to .cursor/mcp.json or .windsurf/mcp.json:

{
  "mcpServers": {
    "esankhyiki-mcp": {
      "command": "npx",
      "args": ["mcp-remote", "https://mcp.mospi.gov.in/"]
    }
  }
}

Antigravity

Add to your Antigravity MCP settings:

{
  "mcpServers": {
    "mospi_api": {
      "serverUrl": "https://mcp.mospi.gov.in/"
    }
  }
}

Verify Connection

curl -s -X POST https://mcp.mospi.gov.in/ \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-03-26","capabilities":{},"clientInfo":{"name":"test","version":"0.1"}}}'

A successful response returns serverInfo with "name": "MoSPI Data Server".

Local Server

If running locally:

claude mcp add esankhyiki-mcp --transport http http://localhost:8000/mcp

Or with the FastMCP Python client:

import asyncio
from fastmcp import Client

async def main():
    async with Client("http://localhost:8000/mcp") as client:
        overview = await client.call_tool("list_datasets", {})
        print(overview)

asyncio.run(main())

Deployment

Docker

# Build the image
docker build -t mospi-mcp .

# Run the container
docker run -d -p 8000:8000 --name mospi-server mospi-mcp

Docker Compose

Includes Jaeger for distributed tracing visualization:

docker-compose up -d

Services:

MoSPI Server: http://localhost:8000/mcp
Jaeger UI: http://localhost:16686

FastMCP Cloud

Push code to GitHub
Sign in to FastMCP Cloud
Create project with entrypoint mospi_server.py:mcp

Architecture

mospi-mcp-api/
Γö£ΓöÇΓöÇ mospi_server.py          # FastMCP server - tools, validation, routing
Γö£ΓöÇΓöÇ mospi/
Γöé   ΓööΓöÇΓöÇ client.py            # MoSPI API client - HTTP requests to api.mospi.gov.in
Γö£ΓöÇΓöÇ swagger/                 # Swagger YAML specs per dataset (source of truth for params)
Γöé   ΓööΓöÇΓöÇ swagger_user_*.yaml
Γö£ΓöÇΓöÇ observability/
Γöé   ΓööΓöÇΓöÇ telemetry.py         # OpenTelemetry middleware for tracing
Γö£ΓöÇΓöÇ tests/                   # Pytest suite (covering all 25 datasets)
Γö£ΓöÇΓöÇ Dockerfile               # Production container with OTEL instrumentation
Γö£ΓöÇΓöÇ docker-compose.yml       # Full stack with Jaeger
ΓööΓöÇΓöÇ requirements.txt

Design Principles

Principle	Implementation
Swagger as Source of Truth	API parameters validated against YAML specs in `swagger/`, not hardcoded
Auto-routing	CPI routes to Group/Item endpoint based on filters; IIP routes to Annual/Monthly
Validation First	All filters validated before API calls with clear error messages
LLM-Optimized	Tool docstrings document parameters, return values, and workflow sequence

Testing

pip install -r tests/requirements-test.txt
pytest tests/ -v -p no:anyio

Runs in-process against the MCP server (no running server needed). Covers all 25 datasets across all 4 tools. See CONTRIBUTING.md for details.

Configuration

Environment variables for OpenTelemetry:

Variable	Description	Default
`OTEL_SERVICE_NAME`	Service name in traces	`mospi-mcp-server`
`OTEL_EXPORTER_OTLP_ENDPOINT`	OTLP collector endpoint	`http://localhost:4317`
`OTEL_EXPORTER_OTLP_PROTOCOL`	Protocol (`grpc` or `http/protobuf`)	`grpc`
`OTEL_TRACES_EXPORTER`	Exporter type (`otlp`, `console`, `none`)	`otlp`

See .env.example for full configuration options.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines on:

Adding new datasets
Project structure
Development setup
Code style

Resources

MoSPI Open APIs - Official API documentation and e-Sankhyiki portal
FastMCP Documentation - MCP framework docs
Model Context Protocol - MCP specification

License

This project is licensed under the MIT License - see the LICENSE file for details.

DIID

The Data Innovation Lab aims to promote innovation and the use of Information Technology in official statistics, including modernizing survey methods. It seeks to address the current challenges faced by the National Statistical System (NSS). The lab will serve as a platform for testing and developing new ideas through proof-of-concept projects. It will foster collaboration with a wide range of participants such as entrepreneurs, researchers, start-ups, academic institutions, and renowned national and international organizations. By creating an open and dynamic environment, the lab will support the advancement of statistical systems and help improve the quality and efficiency of data collection and analysis.

Know more: https://www.datainnovation.mospi.gov.in/home

Acknowledgments

Made in partnership with Bharat Digital in pursuit of modernising and humanising how governments use technology in service of the public.

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

2hResponse time

3wRelease cycle

4Releases (12mo)

Commit activity

Issues opened vs closed

Resources

GitHub Repository

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MoSPI MCP Server

Table of Contents

Overview

Datasets

MCP Tools

Quick Start

Installation

Running the Server

Connecting from CLI Tools

Claude Code

Cursor / Windsurf

Antigravity

Verify Connection

Local Server

Deployment

Docker

Docker Compose

FastMCP Cloud

Architecture

Design Principles

Testing

Configuration

Contributing

Resources

License

DIID

Acknowledgments

Maintenance

Resources

Looking for Admin?

Latest Blog Posts

MCP directory API