Use Case: Token Security Checks for AI Agents

Your AI agent needs to answer one question before a user swaps: is this token safe?

That sounds simple, but answering it correctly means checking the token contract for honeypot traps, verifying the dApp is not a phishing site, and simulating the transaction to predict the outcome - all before the user signs anything. Miss one step and your agent either blocks legitimate trades or greenlights a drain.

Without Hive

Building this yourself means stitching together at least two providers with completely different auth models, schemas, and failure modes.

GoPlus for token security - sign up, get an API key, learn their query-parameter schema, handle their rate limits and error format.

Tenderly for transaction simulation - a separate account, a separate API key, a project-scoped URL, a different auth header (X-Access-Key), and a different response shape.

Then you write custom risk-scoring logic to combine both results into a single safe/unsafe decision.

import requests

# --- Provider 1: GoPlus token security ---
goplus_resp = requests.get(
    "https://api.gopluslabs.io/api/v1/token_security/1",
    params={
        "contract_addresses": "0xdAC17F958D2ee523a2206206994597C13D831ec7"
    },
)
goplus_data = goplus_resp.json()

is_honeypot = goplus_data["result"]["0xdac17f958d2ee523a2206206994597c13d831ec7"]["is_honeypot"]
buy_tax = goplus_data["result"]["0xdac17f958d2ee523a2206206994597c13d831ec7"]["buy_tax"]
sell_tax = goplus_data["result"]["0xdac17f958d2ee523a2206206994597c13d831ec7"]["sell_tax"]

# --- Provider 2: Tenderly simulation ---
tenderly_resp = requests.post(
    "https://api.tenderly.co/api/v1/account/ME/project/MY_PROJECT/simulate",
    headers={"X-Access-Key": "TENDERLY_API_KEY"},
    json={
        "network_id": "1",
        "from": "0xYOUR_WALLET",
        "to": "0xCONTRACT",
        "input": "0xSWAP_CALLDATA",
        "value": "0",
    },
)
sim_data = tenderly_resp.json()

# --- Custom risk logic combining both ---
safe = (
    is_honeypot == "0"
    and float(buy_tax) < 0.1
    and float(sell_tax) < 0.1
    and sim_data["transaction"]["status"]
)

That is roughly 35 lines before you add error handling, retries, or a third provider for phishing detection. You manage two API keys, two auth flows, two response formats, and a hand-rolled scoring function that breaks the moment either provider changes their schema.

With Hive

Two tool calls through one API key. Same endpoint, same auth header, same JSON shape.

import requests

def hive(tool: str, args: dict) -> dict:
    r = requests.post(
        "https://mcp.hiveintelligence.xyz/api/v1/execute",
        headers={"Authorization": "Bearer YOUR_HIVE_API_KEY"},
        json={"tool": tool, "args": args},
        timeout=30,
    )
    r.raise_for_status()
    return r.json()

security = hive("get_token_security", {
    "chainId": "1",
    "contract_addresses": "0xdAC17F958D2ee523a2206206994597C13D831ec7",
})

simulation = hive("simulate_evm_transaction", {
    "network_id": "1",
    "from": "0xYOUR_WALLET",
    "to": "0xCONTRACT",
    "input": "0xSWAP_CALLDATA",
})

Both responses come back in a consistent JSON envelope. Your agent reads the fields it needs - no schema translation, no second set of credentials.

Side-by-side comparison

Available security tools

Hive bundles six security tools behind the same endpoint and auth:

All six are available through the Security & Risk category endpoint (/hive_security_risk/mcp) or the root MCP endpoint.

Why Hive is 10x better here

The security use case is the narrowest wedge where the difference between DIY and Hive is most dramatic:

• One API key vs three. Every additional provider is another secret to rotate, another dashboard to monitor, another billing account to manage.
• One auth flow vs three. GoPlus uses query params. Tenderly uses X-Access-Key. Other providers use Bearer tokens. Hive uses one header for everything.
• Consistent JSON vs three different response formats. Your agent parses one shape instead of writing adapter code per provider.
• Runtime tool discovery vs static documentation. Your agent can call tools/list to find new security tools as Hive adds them - no code changes, no re-reading docs.

For an AI agent that needs to answer "is this safe?" in real time, the fewer moving parts the better. Hive collapses the security stack into one connection.

Related

• All Tools
• Security & Risk Tools
• Use Cases
• API Integration