// llamaindex integration

Financial context for
LlamaIndex agents.

TickerAPI provides pre-computed market data as MCP tools that LlamaIndex agents can call directly. Categorical responses designed for how language models reason about financial data.

try for free read the docs

// setup

Four lines to market data.

Use llama-index-tools-mcp to connect TickerAPI's remote MCP server. Every tool is available to your agent immediately.

python

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

mcp_client = BasicMCPClient("https://mcp.tickerapi.ai/", headers={
    "Authorization": "Bearer tapi_your_api_key"
})
mcp_tools = McpToolSpec(client=mcp_client)

tools = mcp_tools.to_tool_list()

Pass tools to any LlamaIndex agent. Each tool maps to a TickerAPI endpoint with typed parameters.

// agent workflow

Multi-step analysis.

Your agent can chain tools — scan for oversold stocks, get a full summary, then compare against peers. Each call returns categorical data the model understands without extra prompting.

python

from llama_index.core.agent import FunctionCallingAgent
from llama_index.llms.anthropic import Anthropic

agent = FunctionCallingAgent.from_tools(
    tools,
    llm=Anthropic(model="claude-sonnet-4-20250514"),
    verbose=True
)

response = agent.chat(
    "Find oversold tech stocks, then give me a detailed summary of the top pick and compare it against its main competitor."
)

agent internals

# Agent calls: scan_oversold(sector="Technology")
# → Returns list of oversold tech tickers

# Agent calls: get_summary("NVDA")
# → Full categorical breakdown: trend, momentum, fundamentals

# Agent calls: compare_assets(tickers=["NVDA", "AMD"])
# → Side-by-side comparison across all dimensions

// state changes

Track state changes effortlessly.

Most market data APIs return point-in-time snapshots. TickerAPI tracks state transitions — your agent sees what changed, not just what is.

python

response = agent.chat(
    "Check my watchlist for any state changes since yesterday."
)

json

{
  "ticker": "AAPL",
  "changes": [
    {
      "field": "rsi_zone",
      "from": "neutral",
      "to": "oversold"
    },
    {
      "field": "trend",
      "from": "uptrend",
      "to": "downtrend"
    }
  ]
}

// what your agent sees

What your agent sees.

Every tool returns categorical facts — not raw OHLCV data. Your agent can branch on "oversold" without needing to know what RSI > 70 means.

json

{
  "ticker": "NVDA",
  "trend": "strong_uptrend",
  "momentum": {
    "rsi_zone": "overbought",
    "macd_signal": "bullish"
  },
  "volatility": "high",
  "fundamentals": {
    "pe_zone": "above_historical_avg",
    "earnings_surprise": "positive"
  }
}