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Plug Guavy into Your AI Tool

Step-by-step setup for Claude Desktop, ChatGPT, Cursor, VS Code, the Anthropic API, and any LLM framework. Get live crypto data flowing in minutes.

You will need: A free Guavy API key
MCP

Claude Desktop

Native MCP support - 12 crypto tools available directly in Claude.

1

Get your Guavy API key

Sign up free - no credit card needed. Your key is ready instantly.

2

Download Claude Desktop

Download Claude Desktop if you don't have it yet, then install and open it.

3

Open Claude Desktop config

Go to Claude Desktop → Settings → Developer → Edit Config. This opens claude_desktop_config.json.

4

Add the Guavy MCP server

Add the "guavy-crypto" entry inside the "mcpServers" object. If you already have other servers, keep them — just add this alongside. Replace YOUR_GUAVY_API_KEY with your key.

claude_desktop_config.json
{
  "mcpServers": {
    "guavy-crypto": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://data.guavy.com/mcp?key=YOUR_GUAVY_API_KEY"]
    }
  }
}

Requires Node.js installed (Mac: brew install node · Windows: winget install OpenJS.NodeJS · Linux: sudo apt install nodejs npm).

5

Restart Claude Desktop

After restarting, you will see a hammer icon in the chat input bar. Click it — you should see 12 Guavy tools listed.

6

Try it

Ask Claude: "What is the current BTC trade signal?"

Claude will call get_current_action live and return a real Buy/Sell/Hold response.

12 tools Claude can now call
get_market_summary get_recommendations get_recent_buys get_recent_sells get_current_action get_trend_history get_sentiment_history get_recent_briefs get_instrument_analysis search_instruments list_symbols get_article
Sandbox note: The free plan gets all 350+ coins via MCP - not BTC-only. Each MCP call counts as 25 API units (~200 calls/month on the free plan).
GPT

ChatGPT Custom GPT

Import Guavy into any Custom GPT via OpenAPI Actions - all endpoints in 60 seconds

1

Get your Guavy API key

Sign up free and copy your API key from the dashboard.

2

Create a Custom GPT

Go to chatgpt.com/gpts → Create a GPT → Configure → Actions → Create new action.

3

Import the OpenAPI schema

In the schema field, enter this URL - GPT will auto-import all Guavy endpoints:

OpenAPI schema URL
https://data.guavy.com/openapi.json
4

Set authentication

Under Authentication, set Type to API Key, Auth type to Bearer, and paste your Guavy API key.

5

Add these instructions to your GPT

Paste into the Instructions field for best results:

GPT Instructions
Use the Guavy Action for every crypto data request.
Do not use web search or answer from memory for prices, signals, sentiment, news, or recommendations.
Always call the Guavy API for live data - never guess.
When asked about a coin, check sentiment history and current action before responding.
If a Guavy action fails, say that live data was unavailable rather than guessing.
6

Disable web search

Under Capabilities, turn off Web Search so your GPT always uses live Guavy data instead of searching the web.

7

Test it

Ask your GPT: "What are the current crypto buy recommendations?"

MCP

Cursor / VS Code

Add Guavy as an MCP server in Cursor or VS Code GitHub Copilot

Cursor
1

Open Cursor MCP settings

Go to Cursor Settings → MCP → Add MCP Server.

2

Enter server details

  • Name: Guavy Crypto
  • Type: SSE
  • URL: https://data.guavy.com/mcp
  • Header: Authorization: Bearer YOUR_GUAVY_API_KEY
3

Try it in chat

In Cursor Chat: "@guavy-crypto What is the sentiment trend for ETH this week?"

VS Code (GitHub Copilot)
1

Create .vscode/mcp.json in your project

Add this file (replace YOUR_GUAVY_API_KEY):

.vscode/mcp.json
{
  "servers": {
    "guavy-crypto": {
      "type": "sse",
      "url": "https://data.guavy.com/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_GUAVY_API_KEY"
      }
    }
  }
}
2

Reload VS Code

Copilot will auto-discover the 12 Guavy tools. Use them in the Copilot Chat sidebar.

Windsurf: Same SSE config pattern as VS Code. Add the Guavy MCP server in Windsurf's MCP settings panel with the URL and Bearer token header.
API

Anthropic API (Direct Tool Use)

Define Guavy as native tools in your Claude API calls - no wrappers needed

1

Install dependencies

bash
pip install anthropic requests
2

Define tools and run an agentic loop

python
import anthropic
import requests

client = anthropic.Anthropic(api_key="YOUR_ANTHROPIC_KEY")
GUAVY_KEY = "YOUR_GUAVY_API_KEY"
GUAVY_BASE = "https://data.guavy.com/api/v1"

tools = [
{
    "name": "get_current_action",
    "description": "Get the current trade signal (Buy/Sell/Hold/Stay) for a coin",
    "input_schema": {
        "type": "object",
        "properties": {
            "symbol": {"type": "string"},
            "strategy": {"type": "string", "enum": ["aggressive", "conservative"]}
        },
        "required": ["symbol", "strategy"]
    }
},
{
    "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_current_action":
    r = requests.get(
        f"{GUAVY_BASE}/trades/get-current-action/{tool_input['symbol']}/{tool_input['strategy']}",
        headers=headers
    )
elif tool_name == "get_market_summary":
    r = requests.get(f"{GUAVY_BASE}/newsroom/get-market-summary", headers=headers)
return r.json()

messages = [{"role": "user", "content": "What is the BTC signal and overall market today?"}]
response = client.messages.create(
model="claude-opus-4-5", max_tokens=1024,
tools=tools, messages=messages
)

while response.stop_reason == "tool_use":
tool_results = []
for block in response.content:
    if block.type == "tool_use":
        result = call_guavy(block.name, block.input)
        tool_results.append({
            "type": "tool_result",
            "tool_use_id": block.id,
            "content": str(result)
        })
messages += [
    {"role": "assistant", "content": response.content},
    {"role": "user", "content": tool_results}
]
response = client.messages.create(
    model="claude-opus-4-5", max_tokens=1024,
    tools=tools, messages=messages
)

print(response.content[0].text)
Full OpenAPI spec at guavy.com/openapi.json - import into any OpenAPI-compatible tool or generate client SDKs automatically.
SDK

LangChain, CrewAI & Any Framework

Two patterns: llms.txt for spec injection, or native MCP via LangChain adapter

Pattern 1 - llms.txt (any framework)
1

Install

bash
pip install anthropic requests
2

Inject the live spec and ask a question

Fetches the API spec at runtime — always current, no hardcoding. Works with any LLM or framework that accepts a system prompt.

python
import os, requests, anthropic

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

# Pull the live spec — updates automatically when Guavy adds endpoints
spec = requests.get("https://data.guavy.com/llms.txt").text

system = f"""You are a crypto analyst. You have access to live market data \
via the Guavy API. Use it to answer every question — never guess prices or signals.

{spec}

Auth: Authorization: Bearer {GUAVY_KEY}
"""

client = anthropic.Anthropic(api_key=ANTHROPIC_KEY)

response = client.messages.create(
model="claude-opus-4-5",
max_tokens=1024,
system=system,
messages=[{"role": "user", "content": "What's the BTC trade signal right now?"}]
)

print(response.content[0].text)
Pattern 2 - Native MCP via LangChain
1

Install dependencies

bash
pip install langchain-mcp-adapters langchain-anthropic langgraph
2

Build a crypto agent that calls live tools

The agent auto-discovers all 12 Guavy tools and decides which to call based on your question.

python
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():
async with MultiServerMCPClient({
"guavy": {
    "url": "https://data.guavy.com/mcp",
    "transport": "sse",
    "headers": {"Authorization": f"Bearer {GUAVY_KEY}"}
}
}) as client:
tools = await client.get_tools()
agent = create_react_agent(ChatAnthropic(model="claude-opus-4-5"), tools)

# Each call picks the right tool automatically
await ask(agent, "What's the overall market doing today?")
await ask(agent, "Which coins have a buy signal right now?")
await ask(agent, "What's the ETH sentiment trend over the past 2 weeks?")

asyncio.run(main())
Claude.ai web (no Desktop): Paste this into a new conversation:

"Please read this API spec and use it to help me with crypto questions: https://data.guavy.com/llms.txt - My Guavy API key is: YOUR_GUAVY_API_KEY"

Claude web will generate API calls for you (it does not execute them directly - use Claude Desktop + MCP for live tool execution).

Your AI tool is one config away from live crypto intelligence

Free sandbox forever. No credit card required. All 350+ coins available via MCP.

Disclaimer: Guavy is a data and market intelligence provider, not an investment advisor. The information, signals, and market analysis provided by the Guavy mobile application, API, and related services are for informational purposes only and are not intended as financial advice, investment recommendations, or an endorsement of any particular trading strategy. Cryptocurrency trading is highly volatile, carries significant risk, and may not be suitable for all investors. Past performance is not indicative of future results. Users should consult with a qualified financial professional before making any investment decisions. Guavy makes no guarantee of trading profits or financial returns.

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