KEY TAKEAWAYS

A structured MCP server gives agents a repeatable interface to Indian-market data instead of relying on ad-hoc browsing.
DalalOS exposes 59 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; watchlist actions 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 59 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 return context computed from fixed formulas.
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

Your agent can chain search, quote, financials, peers, filings or market context inside its loop.

Cite freshness

Market-data results carry 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 figures are read from cache. Filing tools may queue processing and consume quota. No tool places brokerage orders.
The watchlist actions 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 and gives no 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 59 current Indian-market research capabilities over streamable HTTP. Market figures are read from cache. Filing tools can queue processing and use on-demand quota. 2 account-scoped actions only add or remove symbols from the caller's DalalOS watchlist.
What does a market-data response include?: Each response uses a consistent envelope with status, the data itself, and source and freshness context. That keeps agent outputs auditable and lets you gate them on data age.
Is the data suitable for trading bots?: DalalOS is for research and informational workflows and does not execute trades. Quote-style market data is end-of-day and there are no brokerage/order actions, so it fits research agents and does not fit order-routing bots.
What sources back the data?: Official and public sources for 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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