Sources checked August 2026

Screener’s public product is at https://www.screener.in/. Its support documentation describes screen exports for scripts and states that Screener does not provide APIs. Screener’s 2026 changelog also documents Screener AI and Insights, so this page does not frame the product as merely a static website.

KEY TAKEAWAYS

Screener is human-first: company pages, custom screens, watchlists, long financial history, exports and AI-assisted research.
DalalOS is agent-first: a hosted MCP endpoint exposes structured Indian-market tools with source/freshness context.
Screener’s public support docs say there is no API; checked August 2026, we also found no official Screener MCP in its public documentation.
Neither comparison nor DalalOS output is investment advice: DalalOS emits no buy/sell verdicts or target prices.

Capability comparison

Aspect	DalalOS MCP	Screener.in
Primary interface	Remote MCP tools for AI assistants/agents	Web research, screens, exports, AI features
Official MCP	Yes — first-party hosted MCP	Not found in public docs (checked Aug 2026)
Programmatic workflow	Structured MCP tool calls	CSV/Excel export; public support says no API
Company research	Structured financials, ownership, peers, filings and market context	Deep human-facing pages + long history
Screening	AI-callable screening over supported transparent fields	Rich custom query/screen workflow
Source/freshness contract	Returned with market-data responses where available	Product-specific presentation
Quote cadence	Quote-style prices are end-of-day	Check current Screener product
Trading / brokerage actions	None	Research product
Verdicts / target prices	No buy/sell calls or targets	Has its own research/AI experience

Where Screener is stronger

A mature browser experience for reading a company and drilling into years of financial history.
Flexible saved screens and a large ecosystem of public/user-created screen queries.
Excel and CSV workflows for analysts who prefer spreadsheets.
Integrated notes, watchlists, announcements and newer Screener AI/Insights features.

Where DalalOS is different

The integration surface itself is MCP: an AI client discovers tools and calls them instead of automating a web UI.
Market-data responses are designed to expose source and freshness context for traceable AI answers.
The hosted endpoint works as a reusable data layer across compatible AI clients rather than as one research website.
Research outputs stay on the data/mechanical-ratio side: no buy/sell recommendations, target prices or brokerage execution.

Example agent workflows

EXAMPLE PROMPTS

Compare HDFC Bank and ICICI Bank on the latest available financial and ownership metrics, and include source/freshness context.
Screen for companies meeting these raw financial constraints, then fetch the underlying figures for the first five results.
Show promoter holding and pledge history for this company, then list recent filings relevant to the change.

If you specifically want a human research portal, Screener’s interface may be the better starting point. If you want an AI assistant or agent to call Indian-market research tools directly, see the DalalOS tool reference and connection guide.

FAQ

Common questions

Does Screener.in have an official MCP server?

Checked August 2026: we could not find an official Screener.in MCP offering in Screener's public product, guide or support documentation. Screener's own support article says it does not provide APIs and points developers to CSV export instead. Verify Screener's current documentation because integrations can change.

Is DalalOS a replacement for Screener.in?

Not one-for-one. Screener is a mature human-facing research product with long-history company pages, custom screens, exports, notes and its own AI features. DalalOS is designed as an MCP-native data and research layer that an AI assistant can call programmatically.

Can DalalOS place trades or recommend stocks?

No. DalalOS has no brokerage execution and no buy/sell calls, target prices or investment recommendations. It returns sourced facts and mechanically-computed measures for research workflows.

Which should I use with an AI agent?

If the agent needs structured callable tools with source and freshness context, DalalOS is the native fit. If your main workflow is interactive company research, custom screening and Excel/CSV export, Screener remains a strong human-facing research interface.

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