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MoSPI MCP Server

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

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Table of Contents

• Overview
• Datasets
• MCP Tools
• Quick Start
  • Installation
  • Running the Server
  • Connecting from an MCP Client
• Deployment
  • Docker
  • Docker Compose
  • FastMCP Cloud
• Architecture
• Configuration
• Contributing
• Resources
• License
• About DIID
• Acknowledgments

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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:

• 23 statistical datasets covering employment, inflation, industrial production, GDP, energy, renewable energy, higher education, school education, gender, health, 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

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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)	Agricultural households, land ownership, farm income, crop insurance
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
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
<!--	NMKN	National Namkeen Consumption Index

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MCP Tools

The server exposes 4 tools that follow a sequential workflow:

list_datasets  →  get_indicators  →  get_metadata  →  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.

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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())

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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

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

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Architecture

mospi-mcp-api/
├── mospi_server.py          # FastMCP server - tools, validation, routing
├── mospi/
│   └── client.py            # MoSPI API client - HTTP requests to api.mospi.gov.in
├── swagger/                 # Swagger YAML specs per dataset (source of truth for params)
│   └── swagger_user_*.yaml
├── observability/
│   └── telemetry.py         # OpenTelemetry middleware for tracing
├── tests/                   # Pytest suite (covering all 23 datasets)
├── Dockerfile               # Production container with OTEL instrumentation
├── docker-compose.yml       # Full stack with Jaeger
└── 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

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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 23 datasets across all 4 tools. See CONTRIBUTING.md for details.

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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.

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Contributing

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

• Adding new datasets
• Project structure
• Development setup
• Code style

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Resources

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

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License

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

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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.

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