KEY TAKEAWAYS

A structured MCP server gives agents a repeatable interface to Indian-market data instead of relying on ad-hoc browsing.
DalalOS exposes 56 current, non-deprecated capabilities over streamable HTTP across thousands of NSE/BSE companies.
Market-data responses include source and freshness context so agent outputs stay auditable.
Research reads do not place trades; the only writes save or remove symbols from the caller's DalalOS watchlist.

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 every supported capability. DalalOS implements it for Indian markets with 56 current capabilities over streamable HTTP.

What data is available

Stock search, company profiles and source-linked company reports.
End-of-day quotes and price history, including mechanically-computed return context.
Financials and ratios: PE, PB, ROE, ROCE, D/E and operating trends.
Peer comparison sets and purpose-specific relevant peers.
Shareholding patterns, promoter pledge trends and available institutional-flow context.
Dividends, corporate actions, filings, disclosure search and event context.
Screening, sectors, indices, market breadth, derivatives, calendars and macro/reference context.

How agents consume it

Discover capabilities

On connect, the agent sees the current MCP tools and their inputs.

Call in sequence

Search, quote, financials, peers, filings or market context can be chained inside the agent loop.

Cite freshness

Market-data results expose source/freshness context the agent can surface or gate 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://mcp.dalalos.in/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

Market-data research capabilities are read-only and cannot place brokerage orders.
The only writes add or remove symbols from the authenticated caller's DalalOS watchlist.
Official/public source provenance keeps research inputs traceable.
No verdicts — DalalOS supplies sourced facts and mechanically-computed measures, not buy/sell calls.
Use freshness context to decide when data is too old for your workflow.

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 56 current Indian-market research capabilities over streamable HTTP. Market-data capabilities are read-only; 2 account-scoped actions only add or remove symbols from the caller's DalalOS watchlist.

What does a market-data response include?

A consistent envelope with status, the data itself, source and freshness context, so agent outputs remain auditable and can be gated on data age.

Is the data suitable for trading bots?

DalalOS is for research and informational workflows, not trading execution. Quote-style market data is end-of-day and there are no brokerage/order actions, so it suits research agents rather than order-routing bots.

What sources back the data?

Official and public sources appropriate to each supported domain, including NSE archives, BSE official interfaces and SEBI-mandated BSE-XBRL filings, plus named official public-sector sources for supported macro/reference datasets.

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