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// // crewai integration

Market data tools for
CrewAI agents.

TickerDB 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 TickerDB's remote MCP server, get the tools, and assign them to your agents.

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

mcp_server = MCPServerAdapter(
    server_url="https://mcp.tickerdb.com/",
    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 TickerDB tools to specialized crew members. Your analyst monitors the watchlist, your researcher gets detailed summaries, your strategist checks historical events.

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.tickerdb.com/",
    headers={"Authorization": "Bearer tapi_your_api_key"},
)

tools = mcp_server.tools

analyst = Agent(
    role="Market Analyst",
    goal="Monitor watchlists and flag state changes",
    tools=tools,
)

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

scan_task = Task(
    description="Check the watchlist for any notable state changes",
    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. TickerDB 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 categorical snapshot for a single asset — trend, momentum, volatility, volume, extremes, fundamentals, support/resistance.

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.

get_search

Multi-field filtering across all assets. Build complex queries with arbitrary filter combinations.

get_schema

All queryable fields with types, values, and descriptions. Always free.

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 tickerdb + crewai

Data your crew actually understands.

Categorical, not numerical

TickerDB 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 TickerDB 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