NSO Launches Beta MCP Server for Real-Time AI Access to eSankhyiki Data

NSO launches beta MCP server to make government data AI-ready on eSankhyiki

The National Statistics Office has released a beta Model Context Protocol (MCP) server for the eSankhyiki portal, announced by the Ministry of Statistics and Programme Implementation. The goal: make official datasets usable directly inside AI tools so teams can query, analyze, and report without downloading bulky files.

This shift means faster access, live analysis, and quicker briefings for decision-makers. It's a practical step in modernising India's data infrastructure and serving citizens with timely, reliable numbers.

Why this matters for government teams

• Real-time answers: Run on-the-fly queries on official datasets through your AI applications.
• Less friction: Skip manual downloads and data wrangling; focus on insights and decisions.
• Automated reporting: Set up routine outputs and dashboards with minimal effort.
• Cross-dataset analysis: Merge multiple products in one place for richer evidence.
• Transparency and access: A single entry point for validated statistics boosts trust in numbers.

What's available in beta

The initial rollout covers seven key data products, with more to follow based on feedback and system readiness:

• Periodic Labour Force Survey (PLFS)
• Consumer Price Index (CPI)
• Annual Survey of Industries (ASI)
• Index of Industrial Production (IIP)
• National Accounts Statistics (NAS)
• Wholesale Price Index (WPI)
• Environmental Statistics

NSO will expand coverage gradually. The intent is clear: make official datasets easier to use for real-world planning and faster evidence-based policy.

How it works, in plain terms

The MCP server lets AI tools connect directly to eSankhyiki datasets, remove extraction hurdles, and return results to your app. It supports automated reporting, advanced analysis, and dataset joins without heavy setup.

The system is technology-agnostic, so it's built to work with a wide range of AI platforms as they evolve. For policy teams, that means flexibility without vendor lock-in.

What to do now (practical next steps)

• Pick two use cases: Daily price briefs or monthly labour snapshots are good starters. Define metrics and a short pilot timeline (3-4 weeks).
• Stand up a pilot: Connect your chosen AI tool to PLFS or CPI via the MCP server. Set baseline accuracy and turnaround targets.
• Set guardrails: Confirm data access rules, PII handling (if any), audit logs, and approval flows before scaling.
• Ship templates: Create prompt and report templates for recurring tasks-cabinet notes, media briefs, and dashboard refreshes.
• Integrate where work happens: Plug outputs into existing dashboards or MIS. Keep the workflow familiar for faster adoption.
• Close the loop: Share feedback with NSO on latency, schema gaps, and priority datasets to accelerate improvements.

Policy and procurement pointers

• Use the tech-agnostic stance to keep procurement flexible. Favor open standards and clear exit options.
• Assess security: API keys, rate limits, monitoring, and incident response. Set SLAs for uptime and performance.
• Plan interoperability with NIC Cloud, API gateways, and department data catalogs.
• Document usage policies for model choice, validation steps, and human-in-the-loop sign-off.

Timing and governance context

The launch lands ahead of the AI Impact Summit (Feb 15-20, 2026), signaling India's push to bring AI into routine governance. It ties to Working Group 6 on Democratising AI, led by Dr Saurabh Garg at the ministry-moving from policy talk to implementation.

NSO calls the MCP server a core building block for "Viksit Bharat," with quicker access to updated figures and a single entry point for official statistics. Expect steady upgrades through the beta period.

Where to learn more

• Ministry of Statistics and Programme Implementation - official updates and technical documentation.
• Model Context Protocol - protocol overview for teams integrating AI tools.

Building internal capability

If your team is standing up AI workflows around official data, invest in upskilling alongside the pilot. A small, trained core group speeds adoption across divisions.

• AI courses by job role - helpful for policy, data, and IT teams building repeatable practices.