Connect Your AI Tool

https://guavy.com/mcp?key=YOUR_GUAVY_API_KEY

https://guavy.com/openapi.json

Use the Guavy Action for every question about market news, sentiment, prices or other market data.
Do not use web search or answer from memory for news, sentiment, prices, signals, or recommendations.
Always call the Guavy API for this data rather than guessing.
When asked about an asset, check its recent news briefs and sentiment history before responding. For a crypto asset, the trade simulator's current action is also available.
If a Guavy action fails, say that Guavy's data was unavailable rather than guessing.

{
  "servers": {
    "guavy": {
      "type": "sse",
      "url": "https://guavy.com/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_GUAVY_API_KEY"
      }
    }
  }
}

pip install anthropic requests

import json
import anthropic
import requests

client = anthropic.Anthropic(api_key="YOUR_ANTHROPIC_KEY")
GUAVY_KEY = "YOUR_GUAVY_API_KEY"
GUAVY_BASE = "https://guavy.com/api/v2/crypto"

tools = [
    {
        "name": "get_recent_briefs",
        "description": "Get a coin's latest scored news briefs: headline, summary, six scores and the assets each may affect",
        "input_schema": {
            "type": "object",
            "properties": {
                "symbol": {"type": "string", "description": "Coin symbol, e.g. BTC"},
                "limit": {"type": "integer", "description": "How many briefs, newest first"}
            },
            "required": ["symbol"]
        }
    },
    {
        "name": "get_market_summary",
        "description": "Get today's AI-generated crypto market summary",
        "input_schema": {"type": "object", "properties": {}}
    }
]

def call_guavy(tool_name, tool_input):
    headers = {"Authorization": f"Bearer {GUAVY_KEY}"}
    if tool_name == "get_recent_briefs":
        r = requests.get(
            f"{GUAVY_BASE}/newsroom/get-recent-briefs/{tool_input['symbol']}",
            headers=headers, params={"limit": tool_input.get("limit", 5)}
        )
    elif tool_name == "get_market_summary":
        r = requests.get(f"{GUAVY_BASE}/newsroom/get-market-summary", headers=headers)
    else:
        return {"error": f"unknown tool {tool_name}"}
    return r.json()

messages = [{"role": "user", "content": "What's the latest news on BTC, and what's the overall crypto mood today?"}]

while True:
    response = client.beta.messages.create(
        model="claude-opus-5-5", max_tokens=16000,
        # If the model declines, the API retries the request on a fallback model
        betas=["server-side-fallback-2026-07-01"], fallbacks="default",
        tools=tools, messages=messages
    )
    if response.stop_reason != "tool_use":
        break
    messages.append({"role": "assistant", "content": response.content})
    messages.append({"role": "user", "content": [
        {"type": "tool_result", "tool_use_id": block.id,
         "content": json.dumps(call_guavy(block.name, block.input))}
        for block in response.content if block.type == "tool_use"
    ]})

print("".join(block.text for block in response.content if block.type == "text"))

pip install anthropic requests

import os, requests, anthropic

GUAVY_KEY     = os.environ["GUAVY_API_KEY"]
ANTHROPIC_KEY = os.environ["ANTHROPIC_API_KEY"]

# Fetch the spec at run time, so new endpoints reach your agent without a code change
spec = requests.get("https://guavy.com/llms.txt").text

system = f"""You are a developer assistant. Use the Guavy API spec below to write
correct requests: never invent an endpoint, parameter or field.

{spec}

Auth: Authorization: Bearer {GUAVY_KEY}
"""

client = anthropic.Anthropic(api_key=ANTHROPIC_KEY)

response = client.beta.messages.create(
    model="claude-opus-5-5",
    max_tokens=16000,
    # If the model declines, the API retries the request on a fallback model
    betas=["server-side-fallback-2026-07-01"],
    fallbacks="default",
    system=system,
    messages=[{"role": "user", "content": "Write a Python function that prints BTC's five newest briefs with their sentiment."}]
)

print("".join(block.text for block in response.content if block.type == "text"))

pip install langchain-mcp-adapters langchain-anthropic langgraph

import os, asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent

GUAVY_KEY = os.environ["GUAVY_API_KEY"]

async def ask(agent, question):
    result = await agent.ainvoke({"messages": question})
    print(f"\n> {question}")
    print(result["messages"][-1].content)

async def main():
    client = MultiServerMCPClient({
        "guavy": {
            "url": "https://guavy.com/mcp",
            "transport": "streamable_http",
            "headers": {"Authorization": f"Bearer {GUAVY_KEY}"}
        }
    })
    tools = await client.get_tools()
    agent = create_react_agent(ChatAnthropic(model="claude-opus-5-5"), tools)

    # Each call picks the right tool automatically
    await ask(agent, "What's the latest scored news on ETH?")
    await ask(agent, "What's the ETH sentiment trend over the past 2 weeks?")
    await ask(agent, "Which coins have a buy signal from the trade simulator?")

asyncio.run(main())