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Good practice Imported

MoSPI's MCP Server — AI-Ready Natural-Language Access to India's Official Statistics

India · New Delhi · See the India profile · See the New Delhi profile

Evidence: Descriptive / self-reported Top 51% 60/100 · Ask Evidence Copilot about this practice

India's National Statistical Office launched a public Model Context Protocol server letting AI assistants query official economic and social statistics in plain language, with source attribution, starting with 7 of its ~19 statistical products.

7 products
Statistical products live in beta (February 2026)
~19 products
Total statistical products planned (2026 roadmap)
MoSPI's MCP Server — AI-Ready Natural-Language Access to India's Official Statistics

Details

Maturity
Pilot
Promoter
Ministry of Statistics and Programme Implementation (MoSPI) / National Statistical Office, with Bharat Digital
Period
2026–ongoing
Keywords
official statistics, open data, AI infrastructure, public administration

Context

In February 2026, India's National Statistical Office (part of the Ministry of Statistics and Programme Implementation, MoSPI) launched a beta Model Context Protocol (MCP) server for its eSankhyiki statistics portal, developed with nonprofit partner Bharat Digital, allowing AI assistants such as Claude or ChatGPT to query official economic and social statistics through natural language with built-in source attribution.

Activities

Users can add mcp.mospi.gov.in as a custom connector in Claude or ChatGPT, including on free tiers, to ask plain-language questions about employment, prices, industrial output or GDP. The beta phase covers seven of about 19 planned statistical products — including the Periodic Labour Force Survey, Consumer Price Index, Annual Survey of Industries, Index of Industrial Production, National Accounts Statistics (GDP), Wholesale Price Index and Environmental Statistics — and the source code is published on GitHub under an MIT licence.

Results

Independent testing found that figures returned by the server matched official press releases and bulletins. Known limitations include difficulty handling large data subsets and a lack of automatic warnings about base-year revisions or series discontinuities, making the tool better suited to headline lookups than deep time-series analysis.

Implementation

Indicative cost
Low (< €50k)
Time to results
Short (< 1 year)
Staffing & skills
Ministry of Statistics and Programme Implementation (MoSPI) / National Statistical Office, Nonprofit partner Bharat Digital

Conditions for success

  • Open-source code (MIT licence) enabling external scrutiny
  • Source attribution built into responses
  • Phased rollout of datasets (7 of ~19 products in beta) rather than a full launch

Common failure modes

  • Difficulty handling large data subsets
  • No automatic warnings about base-year revisions or series discontinuities, limiting suitability for deep time-series analysis

Where it fits

Governance type
national statistical office
Scale
national
Income level
middle-income

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