I need a connector that ingests real-time stock market data for an LLM financial analyst.

Yes. For an LLM financial analyst, I’d build the connector around a market-data provider with both streaming and historical APIs, then expose a small set of analyst-oriented tools to the model.

Best fit: Finnhub

Finnhub is particularly attractive because it already offers real-time market data plus fundamentals, earnings, estimates, SEC filings, news, economic data, and alternative data. More importantly, as of August 2026 it provides an MCP server specifically designed for AI assistants.

A good architecture would be:

Exchange / Market Data Provider
          │
          ▼
   Streaming ingestion
   (WebSocket / feed)
          │
          ▼
   Normalization layer
   ├─ trades
   ├─ quotes
   ├─ OHLCV
   ├─ corporate actions
   └─ timestamps / venues
          │
          ├──────────────► Time-series store
          │                (recent + historical)
          │
          ▼
     Analyst API / MCP
          │
          ▼
        LLM
          │
   ┌──────┼─────────┐
   ▼      ▼         ▼
 Quote  Fundamentals  News/filings

Tools I'd expose to the LLM

Keep the model-facing interface semantic rather than exposing raw vendor endpoints:

• get_quote(symbol) — bid/ask, last trade, volume, timestamp
• get_intraday(symbol, interval) — intraday OHLCV
• get_market_snapshot(symbols) — batch quotes
• get_fundamentals(symbol) — financial statements and ratios
• get_estimates(symbol) — consensus EPS/revenue and revisions
• get_earnings(symbol) — historical/upcoming earnings
• get_news(symbol, since) — company/market news
• get_sec_filings(symbol, forms) — 10-K, 10-Q, 8-K, etc.
• get_corporate_actions(symbol) — splits/dividends
• screen_stocks(criteria) — valuation, growth, momentum, etc.
• get_historical_prices(symbol, start, end, interval)

Finnhub's current MCP implementation already exposes roughly this type of functionality, including quotes, financials, estimates, earnings, SEC filings, news, economic data, and alternative datasets.

One important distinction

If by "real-time" you mean an LLM that can answer "what is AAPL trading at right now?", Finnhub's MCP connector is a very clean solution.

If you mean tick-by-tick ingestion of the entire market into your own infrastructure, I'd use a dedicated streaming market-data feed and put your own ingestion layer in front of the LLM. Market-data licensing/redistribution rights matter considerably here; for example, Polygon's market-data terms explicitly restrict commercial redistribution and application use under its standard terms.

For a production analyst, I'd therefore separate:

Market feed → your data layer → LLM connector

rather than having the LLM directly consume an unbounded WebSocket stream.

If you're building this as a connector

I can also design the actual MCP connector specification for you—tools, JSON schemas, streaming architecture, caching, timestamp/market-session handling, and an example implementation in Python/TypeScript. That would give you something you can plug directly into an LLM financial analyst.

For an LLM financial analyst, the best choice is Finnhub because it offers lightweight, developer-friendly real-time WebSockets and REST APIs alongside alternative data like earnings transcripts and sentiment analysis that LLMs can easily parse.

Top Real-Time Market Data Connectors

• Finnhub Stock API : Best for developer ease , offering real-time WebSocket streaming for stocks, forex, crypto, and fundamental data.
• EODHD : Best for AI-era integrations , featuring official Model Context Protocol (MCP) support that connects directly to LLMs like Claude and ChatGPT.
• Polygon.io : Best for high-frequency U.S. market infrastructure , providing low-latency raw trade and quote data.
• Alpaca API : Best if your LLM needs to execute trades or test strategies through a paper-trading brokerage workflow.

Quick Comparison

If you'd like, let me know:

• Whether you are building a streaming real-time agent or a batch-fetching tool
• If you need execution/trading capabilities alongside data ingestion

I can provide a tailored code snippet or architecture layout for your stack.

If you mean a connector specifically for an LLM financial analyst, I’d use an MCP-based market-data connector, rather than building a generic REST connector.

Best options

• ny5.massive.com — my top choice for a production analyst. It provides real-time WebSockets plus an official MCP server, and is explicitly designed for AI agents/LLM applications.
• mcp.alphavantage.co — easiest hosted MCP option. Its official server exposes real-time and historical market-data functions directly as MCP tools.
• tickerlayer.com — particularly convenient if you want one connector covering stocks, ETFs, indices, commodities, FX, crypto and bonds. It offers typed tools and a hosted MCP endpoint.
• docs.alpaca.markets — excellent if you're willing to build the MCP layer yourself. Its WebSocket feed delivers real-time trades, quotes and bars, with SIP and IEX feeds available depending on subscription.
• github.com — better when the analyst needs research context as well as prices: analyst ratings, price targets, technicals, options, insider/hedge-fund activity, earnings and news.

What I'd build

For an LLM financial analyst, I'd expose a small set of high-level tools rather than dumping raw ticks into the model:

get_quote(symbol)
get_market_snapshot(symbols)
get_intraday_bars(symbol, interval)
get_historical_prices(symbol, start, end)
get_company_profile(symbol)
get_fundamentals(symbol)
get_earnings(symbol)
get_news(symbol, since)
get_analyst_estimates(symbol)
get_options_snapshot(symbol)

Then have the connector normalize everything into a consistent schema:

symbol
timestamp
price
bid
ask
volume
market_status
source
data_latency

Important architecture point: don't continuously stream every tick into the LLM context. Put the real-time feed into a cache/event store, maintain the latest snapshot and relevant rolling windows, and let the LLM retrieve the specific data it needs. That keeps token usage and latency under control while preserving real-time information.

If you want zero-code setup, I'd start with Massive's MCP or Alpha Vantage MCP. If you want a production-grade custom connector, I'd use Massive or Alpaca as the data feed and build a thin MCP server on top. Alpaca explicitly supports real-time WebSocket market data and SDKs for Python, Go, Node.js and C#.

If by “connector” you mean a connector I can actually install/configure for you in this environment, tell me which LLM/agent you're using (OpenAI, Claude, Cursor, etc.), and I can narrow it to the appropriate integration.

To connect real-time stock market data to an LLM financial analyst, the modern standard is the Model Context Protocol (MCP) . Instead of writing custom JSON/REST wrappers or polling loops, you can plug in a native MCP server that exposes financial functions directly as tools to your LLM framework or chat client (like Claude or custom agent builds).

The primary plug-and-play options and traditional API approaches break down as follows:

• Model Context Protocol (MCP) Server: The Alpha Vantage MCP Server is the premier plug-and-play choice for LLMs. It natively exposes tools like real-time quotes, technical indicators (RSI, moving averages), and company fundamentals directly to LLM environments without custom middleware.
• Traditional REST/Websocket APIs (Custom Tool-Calling): If you are building a custom backend (e.g., using LangChain, LlamaIndex, or AutoGen), providers like Alpha Vantage, Polygon.io , or Alpaca offer robust REST endpoints and WebSockets that you can wrap as custom Python tool functions.
• Lightweight Python Scraping/Libraries: For rapid prototyping or non-enterprise workloads, packages like yfinance or Stockdex can pull live or same-day data into a pandas dataframe for your LLM context window.

Recommended Setup Approach

1. Grab an API Key: Sign up for a free or developer tier key at Alpha Vantage.
2. Configure the Connector: If using an MCP-compatible client (like Claude Desktop or an agent runner), add the remote MCP endpoint with your embedded API key.
3. Define the Analyst Loop: Let the LLM dynamically call functions like GLOBAL_QUOTE or COMPANY_OVERVIEW whenever a user asks a real-time question about a ticker.

To help narrow down the ideal integration, tell me:

• Are you building a custom code application (Python/LangChain) or using an out-of-the-box LLM interface (like Claude or ChatGPT)?
• Do you need tick-by-tick WebSocket streaming or just on-demand price checks?

Yes. For an LLM financial analyst, I’d build the connector around a provider that offers both real-time streaming and structured historical/fundamental data, rather than treating the LLM as the market-data ingestion layer.

My top pick: twelvedata.com

It is unusually well suited to an LLM analyst because it now explicitly provides an AI/LLM-oriented data layer, including a native MCP server, AI-friendly documentation, structured JSON, WebSockets for live data, and fundamentals/technical analytics.

The connector architecture I'd use is:

              ┌─────────────────────┐
              │   Twelve Data       │
              │ REST + WebSocket    │
              └──────────┬──────────┘
                         │
                  real-time events
                         │
                         ▼
              ┌─────────────────────┐
              │ Market Data          │
              │ Connector            │
              │                     │
              │ normalize            │
              │ timestamp            │
              │ validate             │
              │ deduplicate          │
              └──────────┬──────────┘
                         │
             ┌───────────┴───────────┐
             ▼                       ▼
      ┌──────────────┐       ┌────────────────┐
      │ Redis / Kafka│       │ Time-series DB │
      │ live state   │       │ historical     │
      └──────┬───────┘       └───────┬────────┘
             │                       │
             └───────────┬───────────┘
                         ▼
              ┌─────────────────────┐
              │ LLM Analyst / MCP   │
              │                     │
              │ quote               │
              │ OHLCV               │
              │ fundamentals        │
              │ technicals          │
              │ corporate actions   │
              └─────────────────────┘

What the connector should expose

I'd make the LLM-facing interface deliberately small:

• get_quote(symbol)
• get_snapshot(symbols[])
• get_intraday(symbol, interval)
• get_historical(symbol, start, end)
• get_company_profile(symbol)
• get_fundamentals(symbol)
• get_earnings(symbol)
• get_dividends(symbol)
• get_splits(symbol)
• get_technical_indicator(symbol, indicator, parameters)
• subscribe_quotes(symbols[])
• unsubscribe_quotes(symbols[])

The streaming layer can consume Twelve Data's WebSocket feed; its documentation currently specifies a WebSocket endpoint and symbol subscription mechanism, while its REST API provides quote, time-series and other market-data endpoints.

Important caveat about "real-time"

Don't simply label every incoming price "the market price." Twelve Data says its default US real-time feed represents approximately 5% of total US trading volume, sourced from venues that permit redistribution; broader consolidated US exchange coverage requires additional licensed access.

So I'd put this metadata on every observation:

{
  "symbol": "AAPL",
  "price": 231.42,
  "timestamp": "2026-08-21T19:42:13.421Z",
  "currency": "USD",
  "market": "US",
  "data_type": "real_time",
  "source": "twelve_data",
  "coverage": "redistributable_venues",
  "latency_ms": 170
}

That distinction is particularly important for an LLM: otherwise it can confidently say something like "AAPL is currently trading at..." when the feed isn't actually consolidated across all venues.

Alternative: Polygon/Massive

polygon.io is the other provider I'd seriously consider, particularly if your analyst needs granular US equities trades, quotes, aggregates, options, or market-microstructure data. Its API supports REST plus WebSocket streaming for trades, quotes and aggregates.

I'd choose:

If you're building this as an actual connector/MCP server, I'd use Twelve Data as the first implementation and keep the provider behind an abstract MarketDataProvider interface. That lets you swap in a consolidated/institutional feed later without changing the LLM's tools. Twelve Data already positions its MCP server specifically as a way for AI agents to access live financial data.

If you want, I can also lay out the actual MCP connector specification (tools, schemas, WebSocket ingestion, caching, and failure handling) for this architecture.

To power an LLM financial analyst with real-time stock market data, you need a connector that supports low-latency, event-driven streaming (via WebSockets ) alongside structured JSON or tool-calling frameworks (like Model Context Protocol / MCP ) so the LLM can easily parse the incoming market context.

Top options for real-time stock connectors range from AI-native servers to institutional-grade APIs:

• Alpha Vantage MCP Server — Offers native integration for AI agent ecosystems through the Model Context Protocol (MCP), allowing large language models to directly query real-time quotes and historical data as plug-and-play tools.
• Financial Modeling Prep (FMP) — Provides a robust combination of real-time WebSocket streaming feeds, deep fundamental financial statements, and native MCP server support, making it ideal for deep analytical LLMs.
• EODHD (End of Day Historical Data) — Features a full MCP server, structured OpenAPI specs, and sub-second WebSocket streaming for global stocks, forex, and crypto packaged with extensive fundamental history.
• Alpaca Markets API — An exceptional developer-first choice for streaming real-time U.S. equity data (via IEX or full SIP) using lightweight Python SDKs and WebSockets, especially if your agent pipeline transitions into paper/live trading.
• Finnhub Stock API — Delivers real-time trade streaming, company sentiment, and fundamental data through clean JSON endpoints and WebSocket trade feeds.

To help narrow down the best choice, tell me:

• Are you focusing purely on U.S. equities or do you need international markets?
• Do you prefer an AI-native protocol (MCP) or a direct Python WebSocket script?

To build a robust real-time ingestion connector for an LLM financial analyst, you need a setup that combines a low-latency WebSocket streaming feed for live ticks/quotes with a structured context/memory window (like a sliding buffer or vector/time-series store) so the LLM doesn't get flooded or starved of relevant chronological state.

Top-tier API options equipped with developer-friendly WebSocket support, fundamental datasets, and AI-ready formats include:

• Financial Modeling Prep (FMP) : Excellent choice for AI workflows; provides unified REST and WebSocket endpoints covering global stocks, fundamentals, and increasingly native tool/MCP (Model Context Protocol) alignment.
• Finnhub : Offers developer-first real-time stock price WebSockets alongside sentiment data, earnings call transcripts, and fundamental financials.
• EODHD : Highly regarded for combining deep historical data (30+ years) with sub-second WebSocket streaming and AI-agent/MCP extensions.
• Massive (formerly Polygon.io) : The gold standard for ultra-low latency, granular U.S. trade/quote (SIP/ مباشر) streaming feeds and extensive developer SDKs.

Recommended Architecture Pattern

1. Ingestion Worker (Async Python): Maintain an asynchronous websockets or websocket-client connection to stream live JSON payloads.
2. Sliding Window Buffer: Pipe incoming ticks into an in-memory ring buffer or a lightweight local store (like pandas or SQLite ) to aggregate OHLCV bars over 1-minute or 5-minute intervals . Do not pass raw sub-second tick-by-tick noise directly to the LLM context window.
3. LLM Context Injection: Expose the aggregated state to your LLM agent via function calling or custom tools whenever the analyst requests a market update or triggers an alert.

To help tailor the exact code implementation or select the right provider, tell me:

• Are you focusing purely on U.S. equities or global/multi-asset coverage?
• Do you need raw tick data or aggregated minute bars (OHLCV) for the LLM?
• Which framework or orchestration layer (e.g., LangChain, LlamaIndex, custom async loop) are you using?

Yes. For an LLM financial analyst, I’d separate “real-time market data” from “LLM-friendly financial intelligence.”

Best fits

My recommendation

If you're building an agent rather than a trading terminal, I'd start with Alpha Vantage MCP. Its official MCP server exposes financial functions directly as MCP tools, so an LLM can discover and call them without you having to build an MCP wrapper yourself.

For a more serious institutional system, LSEG is considerably more compelling. Its connector provides real-time financial data plus fundamentals, news, estimates, pricing analytics, and other analyst-oriented capabilities through MCP; its underlying WebSocket infrastructure supports streaming real-time market data.

For high-frequency/low-latency market data, I'd look at Massive. It advertises tick-level trades, quotes and aggregate bars, WebSockets/REST, and sub-20ms latency, with infrastructure directly connected to exchanges.

Architecture I'd use

Exchange / Market Data Vendor
            │
            ▼
      Streaming Ingestor
      (WebSocket/MQTT)
            │
            ├── Redis/Kafka ──► real-time state
            │
            └── Timescale/ClickHouse ──► historical data
                                      │
                                      ▼
                              Financial Data MCP
                                      │
                                      ▼
                                  LLM Analyst

The important design choice is not to dump the raw tick stream into the LLM. Keep the stream in your data layer and expose high-value tools such as:

• get_quote(symbol)
• get_intraday_bars(symbol, interval)
• get_order_book(symbol)
• get_price_change(symbol, period)
• get_market_snapshot(symbols)
• get_fundamentals(symbol)
• get_earnings(symbol)
• get_company_news(symbol, since)
• get_sector_performance()
• get_historical_prices(symbol, start, end)

That gives the analyst fresh, deterministic data on demand, while keeping token consumption manageable.

If you tell me whether you're building this as an MCP server, OpenAI connector, REST API, or a backend service, I can recommend the specific vendor + connector architecture and authentication flow.

For an LLM financial analyst, you’ll usually want a market-data connector layer rather than just a raw API client. The connector should stream quotes into a normalized event store, then expose structured context to the LLM through tools/RAG.

A practical architecture:

Exchange feeds
     |
     v
Market Data Connector
     |
     +--> Normalizer (symbols, timestamps, corporate actions)
     |
     +--> Stream processor (Kafka/Pulsar/Redis Streams)
     |
     +--> Time-series DB (TimescaleDB/ClickHouse)
     |
     +--> Feature layer
     |       - price changes
     |       - VWAP
     |       - volatility
     |       - indicators
     |       - earnings/events
     |
     v
LLM Financial Analyst Tools
     |
     +--> get_quote()
     +--> get_order_book()
     +--> get_candles()
     +--> compare_peers()
     +--> summarize_market_move()

Data connector capabilities to prioritize

1. Real-time streaming

• WebSocket/MQTT feed ingestion
• Tick trades
• Bid/ask quotes
• Level 1 and Level 2 order book
• Intraday bars

Examples:

• Alpaca provides real-time stock WebSocket streams for trades, quotes, and other market data feeds.
• Webull provides streaming market data over MQTT/WebSocket, including quotes, snapshots, and tick data.

2. LLM-friendly normalization

Convert provider-specific payloads into a canonical schema:

{
  "symbol": "AAPL",
  "timestamp": "2026-08-14T09:31:05.123Z",
  "event_type": "quote",
  "bid": 225.10,
  "ask": 225.12,
  "last": 225.11,
  "volume": 1829301,
  "exchange": "NASDAQ"
}

For LLM consumption, add derived fields:

{
  "symbol": "AAPL",
  "move_5m_pct": 1.8,
  "relative_volume": 2.4,
  "volatility_state": "elevated",
  "news_sentiment": "negative",
  "analyst_summary": "Selling pressure increased after earnings guidance."
}

3. Event-driven connector API

Expose tools such as:

market.get_quote(symbol)
market.get_intraday(symbol, interval)
market.get_orderbook(symbol, depth)
market.get_market_summary()
market.detect_anomalies(symbol)

The LLM should not consume raw ticks directly; it should ask for interpreted market state.

Provider choices by use case

Recommended MVP stack

• Ingestion: Python async WebSocket workers
• Queue: Kafka or Redis Streams
• Storage: TimescaleDB
• Cache: Redis
• Embeddings: earnings calls, filings, news, research notes
• LLM tools: OpenAI function/tool calling interface
• Monitoring: feed latency, dropped ticks, stale symbols

Important financial-data concerns

• Check redistribution/licensing rights before exposing live data to users.
• Preserve exchange timestamps; don’t rely only on ingestion time.
• Build reconnect + replay logic.
• Store raw events for auditability.
• Separate “market facts” from model-generated analysis.

A strong production connector is essentially a real-time market data MCP/tool server: the LLM calls analytical functions, while the connector handles the complexity of feeds, normalization, and freshness.

To feed real-time stock market data into an LLM financial analyst, you need an architecture that bridges a streaming data source (WebSocket) with an agentic tool-calling or context-injection layer (such as LangChain, LlamaIndex, or direct LLM function calling).

The ideal setup depends on whether your LLM needs to actively pull data on-demand (via a tool) or react to a continuous stream of live ticks.

Top Real-Time Financial Data Connectors for AI

• Polygon.io API — Best ultra-low latency (~25 ms) US equity and options data with robust Python SDKs and native WebSocket streaming support.
• Financial Modeling Prep (FMP) API — Excellent if your LLM analyst needs a blend of real-time price feeds alongside deep fundamental data (balance sheets, transcripts, ratios).
• Alpha Vantage API — Highly popular for AI agents due to its built-in Model Context Protocol (MCP) server support, allowing LLMs to query live and historical quotes plug-and-play style.
• Finnhub API — Great developer-friendly option with rapid global stock, forex, and crypto websocket streaming at low latencies.

Recommended Architecture: Tool-Calling vs. Streaming

1. On-Demand Tool-Calling (Most Common for Analysts)
   • How it works: The LLM receives a user prompt (e.g., "How is AAPL trading right now relative to its daily high?" ). The LLM determines it needs fresh data and calls a custom Python function wrapping a real-time REST endpoint.
   • Why use it: Keeps token usage low and gives the model precise data only when needed.
2. Event-Driven Stream Ingestion (For Live Monitoring)
   • How it works: A persistent Python background worker maintains a WebSocket connection to a provider like Polygon or FMP, buffering the latest trades/quotes into a local state or vector/cache layer (like Redis), which is then injected into the LLM context window or trigger system.

To help tailor the exact code connector or framework setup, let me know:

• Are you building an on-demand tool/agent (like a chatbot) or a continuous background monitor?
• Which LLM framework are you using (e.g., LangChain, LlamaIndex, or raw OpenAI/Anthropic API)?
• Do you have a preferred data provider already selected?