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// // crewai integration

Market data tools for
CrewAI agents.

TickerAPI provides pre-computed financial context via MCP tools. CrewAI's MCP integration lets your crew access market data tools directly — give your analyst agent real market awareness.

try for free read the docs
// setup

Connect in a few lines.

CrewAI supports MCP tool servers via MCPServerAdapter. Point it at TickerAPI's remote MCP server, get the tools, and assign them to your agents.

python 
# Connect CrewAI to TickerAPI's MCP server
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter

mcp_server = MCPServerAdapter(
    server_url="https://mcp.tickerapi.ai/",
    headers={"Authorization": "Bearer tapi_your_api_key"},
)

tools = mcp_server.tools

analyst = Agent(
    role="Market Analyst",
    goal="Analyze market conditions",
    tools=tools,
)
// crew workflow

Give your crew market expertise.

Assign TickerAPI tools to specialized crew members. Your analyst scans for opportunities, your researcher gets detailed summaries, your strategist compares assets.

python 
# Build a crew with market data tools
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter

mcp_server = MCPServerAdapter(
    server_url="https://mcp.tickerapi.ai/",
    headers={"Authorization": "Bearer tapi_your_api_key"},
)

tools = mcp_server.tools

analyst = Agent(
    role="Market Analyst",
    goal="Scan for oversold and breakout opportunities",
    tools=tools,
)

researcher = Agent(
    role="Research Analyst",
    goal="Get detailed summaries on flagged tickers",
    tools=tools,
)

scan_task = Task(
    description="Find oversold tech stocks worth investigating",
    agent=analyst,
)

research_task = Task(
    description="Analyze the top 3 results in detail",
    agent=researcher,
)

crew = Crew(agents=[analyst, researcher], tasks=[scan_task, research_task])
result = crew.kickoff()
// 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 
# Task a crew member with detecting state changes
monitor_task = Task(
    description="Check my watchlist for state changes and summarize what moved",
    agent=analyst,
)
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"
  }
}
// available tools

What your agent can call.

Every tool returns categorical, pre-computed data. Your agent gets facts it can reason about immediately.

get_summary

Full factual snapshot for a single asset — trend, momentum, fundamentals, support/resistance.

compare_assets

Side-by-side technical and fundamental comparison of two or more tickers.

list_assets

Browse all supported tickers with filtering and search.

list_sectors

List all valid sector values with asset counts for scan filtering.

get_watchlist

Live summary data for all tickers in your saved watchlist.

get_watchlist_changes

Field-level diffs for your watchlist since the last pipeline run.

add_to_watchlist

Add tickers to your persistent watchlist.

remove_from_watchlist

Remove tickers from your watchlist.

scan_oversold

Assets in confirmed oversold conditions across multiple indicators.

scan_overbought

Assets in overbought RSI conditions with severity rankings.

scan_breakouts

Momentum breakouts with volume confirmation.

scan_unusual_volume

Volume anomalies and accumulation patterns.

scan_valuation

Historically undervalued or overvalued assets based on fundamental metrics.

scan_insider_activity

Notable insider buying and selling activity.

get_account

Your plan tier, rate limits, and current API usage.

create_webhook

Register a webhook URL for watchlist change notifications.

list_webhooks

List your registered webhook URLs.

delete_webhook

Remove a registered webhook.

full tool reference →
// why tickerapi + crewai

Data your crew actually understands.

Categorical, not numerical

TickerAPI returns "rsi_zone": "oversold" instead of raw RSI values. Your crew reasons on categories it already understands — no prompt engineering required.

One tool per question

Each tool answers a specific question your agent might ask. "What's oversold?" is one tool call, not a chain of raw data fetches and computations.

Tiny context footprint

A TickerAPI response uses a fraction of the tokens you'd need to pass raw OHLCV data. Your crew keeps more context for reasoning, less spent on input.

Start building.

Try for free. No credit card required.

try for free read the docs