Indian stock market data for AI agents

KEY TAKEAWAYS

The cleanest way to give AI agents Indian market data is an MCP server, not scraping or brittle parsing.
DalalOS exposes 20+ read-only tools over streamable HTTP across ~5,000 NSE/BSE companies.
Every response uses one envelope — status · data · source · freshness — so agent outputs stay auditable.
It is read-only and end-of-day: built for research agents, not order-routing trading bots.

Why MCP is a good fit for agents

Agents work by calling tools and reasoning over the results. The Model Context Protocol standardises that interaction, so an agent does not need bespoke glue for each data source. DalalOS implements it for Indian markets: 20+ read-only tools over streamable HTTP, each returning status, data, source and a freshness timestamp.

What data is available

Stock search and company profiles across roughly 5,000 NSE/BSE companies.
End-of-day quotes and prices, with 52-week ranges.
Financials and ratios: PE, PB, ROE, ROCE, D/E — quarterly and annual.
Peer comparison sets for side-by-side analysis.
Shareholding patterns and promoter pledge trends.
Dividends, corporate actions and stock events.
Screening, sector overviews, indices and constituents, and upcoming results.

How agents consume it

Discover tools

On connect, the agent sees the available read-only tools and their inputs.

Call in sequence

Search, then quote, then financials, then peers — chained inside the agent loop.

Cite freshness

Each result carries a timestamp the agent can surface or gate decisions on.

Connect it

Add DalalOS to your agent or MCP client with the server key dalal-os and the streamable HTTP endpoint.

claude_desktop_config.json

{
  "mcpServers": {
    "dalal-os": {
      "url": "https://indianstockdatamcp.duckdns.org/mcp"
    }
  }
}

Design patterns that work

EXAMPLE PROMPTS

Enrich every watchlist name with latest financials and flag rising promoter pledge.
Run a screen, then generate a short sourced brief for each qualifying company.
Assemble a peer-comparison table and attach the data freshness for each row.
Track upcoming results and produce a daily digest of what is due.

Keep it safe and traceable

Read-only by design — agents cannot place trades or mutate anything.
Official sources keep inputs trustworthy; no scraped data enters the loop.
No verdicts — agents report data and ratios, never buy/sell calls.
Use freshness timestamps to decide when data is too old to rely on.

Build on this with the AI agent for Indian stock research use case, or compare delivery models in Indian stock API vs MCP server.

FAQ

Common questions

How do AI agents get Indian stock market data?

Through an MCP server. DalalOS exposes Indian (NSE/BSE) market data as 20+ read-only tools over streamable HTTP, so any MCP-capable agent can call them and receive structured, sourced responses.

What does each response include?

A consistent envelope: status, the data itself, the source, and a freshness timestamp. That makes agent outputs auditable and easy to gate on data age.

Is the data suitable for trading bots?

DalalOS is for research and informational workflows, not trading execution. It is read-only and end-of-day — there are no order/trade actions and no real-time feed — so it suits research agents, not order-routing bots.

What sources back the data?

Official and public sources only: NSE archives (end-of-day bhavcopy), BSE official APIs and SEBI BSE-XBRL filings, covering roughly 5,000 NSE and BSE companies.

Connect your AI to Indian stock market data

Join the waitlist and be first in line when your invite to DalalOS opens.

KEEP EXPLORING