USE CASE · AI AGENTS

Build an AI agent for Indian stock research

An AI agent for Indian stock research is only as good as the data it can reach. DalalOS gives your agent that data: official NSE and BSE market context through an MCP server, exposed as 20+ read-only tools. Point your agent at the endpoint and it can run repeatable, sourced research workflows on Indian equities.

This page leads with the investor outcome — personal AI agents for Indian stock research — with enough technical detail for builders to get going.

Why agents need a dedicated data layer

Autonomous and semi-autonomous agents plan, call tools and synthesise results. For Indian markets, the missing piece is a trustworthy data source the agent can call on every loop. DalalOS is that layer: structured, official, read-only, and consistent. Each response carries status, data, source and a freshness timestamp, which makes agent output auditable.

How it works

01

Give the agent tools

Connect DalalOS so the agent can call search, quotes, financials and more.

02

Define the workflow

Search a name, pull financials, compare peers, read shareholding — in sequence.

03

Synthesise & cite

The agent assembles a sourced research note that always shows data freshness.

Workflows you can automate

  • Watchlist monitoring: refresh quotes, ratios and upcoming results on a schedule.
  • Company briefs: search → profile → financials → peers → shareholding, assembled into a note.
  • Screening pipelines: filter the market by ROE, ROCE, PE or D/E, then enrich the hits.
  • Sector scans: pull a sector overview and drill into constituents.
  • Event tracking: surface dividends, corporate actions and results calendars.

Example agent prompts

EXAMPLE PROMPTS

  • For each name on my watchlist, fetch the latest financials and flag any with rising promoter pledge.
  • Screen mid-caps with ROCE above 20% and D/E below 0.5, then write a one-paragraph brief for the top five.
  • Build a peer-comparison table for three private banks and note data freshness.
  • List all Nifty 500 results due this week and summarise each company's latest quarter.

Safety by design

  • Read-only tools mean an agent can never place a trade or mutate data.
  • Official sources keep inputs trustworthy and traceable.
  • No verdicts: the agent reports data and ratios, never a buy/sell call.
  • Freshness timestamps let you gate decisions on data age.

Dig into the data model in Indian stock market data for AI agents, or read the MCP server overview.

FAQ

Common questions

Can I build an AI agent for Indian stock research?

Yes. Connect your AI agent to DalalOS, an MCP server exposing 20+ read-only tools over streamable HTTP, and the agent can search, fetch quotes, read financials, compare peers and analyse shareholding for NSE/BSE companies.

Do agents get trading or order access?

No. DalalOS is strictly read-only — there are no order, trade or write actions. Agents only read official market data and mechanically-computed ratios. There are no buy/sell calls or target prices.

Why use MCP for an agent instead of scraping?

MCP gives the agent structured, consistent tool responses (status, data, source, freshness) from official sources. That is more reliable and safer than scraping, and it keeps every answer traceable.

Is the data real-time?

No. Prices are end-of-day; financials arrive within the filing cycle; shareholding updates quarterly. Build agent logic around end-of-day cadence, not intraday ticks.

Connect your AI to Indian stock market data

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