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# MoSPI MCP Server
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://gofastmcp.com)
[](https://github.com/nso-india/esankhyiki-mcp/actions/workflows/test.yml)
MCP (Model Context Protocol) server for accessing India's Ministry of Statistics and Programme Implementation (MoSPI) data APIs. Built with FastMCP 3.3.
---
## Table of Contents
- [Overview](#overview)
- [Datasets](#datasets)
- [MCP Tools](#mcp-tools)
- [Quick Start](#quick-start)
- [Installation](#installation)
- [Running the Server](#running-the-server)
- [Connecting from an MCP Client](#connecting-from-an-mcp-client)
- [Deployment](#deployment)
- [Docker](#docker)
- [Docker Compose](#docker-compose)
- [FastMCP Cloud](#fastmcp-cloud)
- [Architecture](#architecture)
- [Configuration](#configuration)
- [Contributing](#contributing)
- [Resources](#resources)
- [License](#license)
- [About DIID](#diid)
- [Acknowledgments](#acknowledgments)
---
## 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:**
- Following statistical datasets covering employment, unemployment, inflation, industrial and services production, GDP and national accounts, energy and renewable energy, higher and school education, gender, health and nutrition, disability, housing and sanitation, household consumption and expenditure, time use, agriculture and livestock, land holdings, debt and investment, AYUSH, environment and climate, banking and financial statistics, economic census, unincorporated enterprises, telecom and digital connectivity.
- Sequential 4-tool workflow designed for LLM consumption
- Swagger-driven parameter validation
- Full OpenTelemetry integration for observability
- Production-ready Docker deployment
- Note: Some NSS rounds include sub-parts (NSS77A, NSS79C, NSS76C, NSS80E), which are shown as separate entries in the README for user understanding. On the server, however, these sub-parts are accessible through their respective parent NSS round dataset (NSS77, NSS79, NSS76, NSS80) itself, rather than as independent datasets.
---
## Dataset Reference
| Sr. No. | Dataset | Full Name | Use For |
| ------: | :----------: | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ |
| 1 | **PLFS** | Periodic Labour Force Survey | Employment, unemployment, labour force participation, worker population ratio, wages, employment by industry and occupation |
| 2 | **CPI** | Consumer Price Index | Retail inflation, cost of living, inflation by commodity group, state-wise and rural/urban price indices |
| 3 | **IIP** | Index of Industrial Production | Industrial growth, manufacturing output, mining, electricity generation, sector-wise production indices |
| 4 | **ASI** | Annual Survey of Industries | Factory performance, industrial employment, wages, fixed capital, output, value added, productivity |
| 5 | **NAS** | National Accounts Statistics | GDP, GVA, national income, sector-wise economic growth, savings, capital formation |
| 6 | **WPI** | Wholesale Price Index | Wholesale inflation, producer prices, commodity price indices, inflation trends |
| 7 | **ENERGY** | Energy Statistics | Energy production, consumption, installed capacity, fuel mix, energy intensity, renewable energy statistics |
| 8 | **AISHE** | All India Survey on Higher Education | Universities, colleges, enrolment, teachers, Gross Enrolment Ratio (GER), Gender Parity Index (GPI), higher education infrastructure |
| 9 | **ASUSE** | Annual Survey of Unincorporated Sector Enterprises | Unincorporated enterprises, MSMEs, employment, output, value added, informal sector statistics |
| 10 | **GENDER** | Gender Statistics | Gender indicators, women empowerment, sex ratio, labour participation, education, health, crimes against women |
| 11 | **NFHS** | National Family Health Survey | Fertility, family planning, maternal and child health, nutrition, infant mortality, health indicators |
| 12 | **ENVSTATS** | Environment Statistics | Climate, biodiversity, forests, air and water quality, environmental resources, pollution indicators |
| 13 | **RBI** | RBI Statistics | Banking, money supply, foreign exchange reserves, exchange rates, balance of payments, external sector, financial indicators |
| 14 | **NSS77** | NSS 77th Round – Land and Livestock Holdings | Agricultural households, land holdings, livestock ownership, crop insurance, farming assets |
| 15 | **NSS77A** | All India Debt and Investment Survey (AIDIS) – NSS 77th Round | Household assets, liabilities, debt, investment, borrowing patterns, wealth distribution |
| 16 | **NSS78** | NSS 78th Round – Multiple Indicator Survey | Drinking water, sanitation, housing amenities, migration, digital connectivity, household living conditions |
| 17 | **CPIALRL** | Consumer Price Index for Agricultural and Rural Labourers | Rural inflation, agricultural labourer cost of living, rural wage index, inflation trends |
| 18 | **HCES** | Household Consumption Expenditure Survey | Household consumption, expenditure patterns, poverty estimation, inequality, consumer behaviour |
| 19 | **TUS** | Time Use Survey | Time allocation, unpaid care work, paid work, household activities, gender time-use patterns |
| 20 | **EC** | Economic Census | Establishments, enterprises, employment, ownership, economic activity, district-wise business statistics |
| 21 | **NSS79** | NSS 79th Round – Survey on AYUSH | AYUSH awareness, AYUSH utilisation, treatment preferences, expenditure on AYUSH services |
| 22 | **NSS79C** | Comprehensive Annual Modular Survey (CAMS) – NSS 79th Round | Education, health expenditure, financial inclusion, digital literacy, household living conditions |
| 23 | **UDISE** | UDISE+ (Unified District Information System for Education Plus) | Schools, enrolment, dropout, teachers, PTR, GER, NER, GPI, CWSN, ICT facilities, school infrastructure |
| 24 | **MNRE** | Renewable Energy Statistics (Ministry of New and Renewable Energy) | Installed renewable energy capacity, solar, wind, hydro, bioenergy, state-wise renewable energy generation |
| 25 | **NSS76** | NSS 76th Round – Drinking Water, Sanitation, Hygiene and Housing Conditions | Drinking water sources, water treatment, sanitation, housing characteristics, toilets, flood experience |
| 26 | **NSS76C** | Persons with Disabilities in India – NSS 76th Round | Disability prevalence, education, employment, accessibility, assistive devices, care arrangements |
| 27 | **NSS75E** | NSS 75th Round – Social Consumption on Education | Literacy, educational attainment, school attendance, education expenditure, internet and computer access, GER/NAR |
| 28 | **NSS80** | NSS 80th Round – Comprehensive Modular Survey: Telecom (CMST) | Mobile phone ownership, internet usage, telecom access, digital services, online banking, cyber security awareness |
| 29 | **NSS80E** | NSS 80th Round – Comprehensive Modular Survey: Education (CMSE) | School enrolment, education expenditure, tuition fees, private coaching, scholarships, sources of education funding |
| 30 | **NSS73** | NSS 73rd Round – Unincorporated Non-Agricultural Enterprises | Enterprise type, enterprise ownership, hired workers, annual emoluments, GVA per worker, employment type, working hours, activity category, sector-wise and state-wise enterprise statistics
| 31 | **NSS74** | NSS 74th Round – Services Sector Enterprises | Service sector enterprises, registration status, establishments by activity, ICT use, workers per establishment, value per establishment, structural ratios | |
| 32 | **NSS71** | NSS 71st Round – Social Consumption: Education in India | Literacy rate, completed level of education, dropout/never-enrolled, attendance and enrolment status, GAR/NAR, general vs technical/vocational education, expenditure per student |
| 33 | **NSS71H** | NSS 71st Round – Social Consumption: Health in India | Hospitalisation cases by age-group/social group/religion/ailment, hospital type and ward distribution, medical expenditure, aged persons' economic independence and mobility, childbirth hospitalisation |
| 34 | **NSS72** | NSS 72nd Round – Household Expenditure on Services and Durable Goods | Household expenditure on transport and miscellaneous consumer services, durable goods acquisition, MPCE, budget share, households having NAE, quintile class of UMPCE |
| 35 | **NSS72T** | NSS 72nd Round – Domestic Tourism Expenditure in India | Overnight and same-day trips, purpose of trip, mode of travel, type of stay, nights spent, tourism expenditure per trip, package vs non-package trips |
| 36 | **ISP** | Index of Service Production | Services sector growth, monthly services output, sub-sector indices |
<!-- | NMKN | National Namkeen Consumption Index | Bhujia per capita, sev consumption patterns, mixture preference by state | -->
---
## 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.
---
## 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/a08376ca-8835-479f-9374-1cca63d5631c
https://github.com/user-attachments/assets/a2d65b8f-c938-43fa-856c-35088063f4bd
To get more information, visit - https://www.datainnovation.mospi.gov.in/mospi-mcp
The instructions below are for self-hosting the MCP server.
### Installation
```bash
# 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
```bash
# 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
```bash
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`:
```json
{
"mcpServers": {
"esankhyiki-mcp": {
"command": "npx",
"args": ["mcp-remote", "https://mcp.mospi.gov.in/"]
}
}
}
```
#### Antigravity
Add to your Antigravity MCP settings:
```json
{
"mcpServers": {
"mospi_api": {
"serverUrl": "https://mcp.mospi.gov.in/"
}
}
}
```
#### Verify Connection
```bash
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:
```bash
claude mcp add esankhyiki-mcp --transport http http://localhost:8000/mcp
```
Or with the FastMCP Python client:
```python
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
```bash
# 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:
```bash
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](https://fastmcp.cloud)
3. 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 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
```bash
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 datasets across all 4 tools. See [CONTRIBUTING.md](CONTRIBUTING.md#testing) 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](CONTRIBUTING.md) for guidelines on:
- Adding new datasets
- Project structure
- Development setup
- Code style
---
## Resources
- [MoSPI Open APIs](https://api.mospi.gov.in) - Official API documentation and e-Sankhyiki portal
- [FastMCP Documentation](https://gofastmcp.com) - MCP framework docs
- [Model Context Protocol](https://modelcontextprotocol.io) - MCP specification
---
## License
This project is licensed under the MIT License - see the [LICENSE](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](https://bharatdigital.io)** in pursuit of modernising and humanising how governments use technology in service of the public.
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