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0xsl1m

cerebrus-pulse-mcp

cerebrus_screener

Scan 30+ coins for top trading signals including RSI zone, trend, volatility, funding bias, confluence score, and OI trend at a low cost.

Instructions

Scan all 30+ coins for top trading signals. Returns RSI zone, trend, volatility regime, funding bias, multi-TF confluence score with alignment, and OI trend for each coin. Much cheaper than calling pulse individually. Cost: $0.04 USDC via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNoNumber of top coins to return (1-100). Default: 30

Implementation Reference

  • Registration of the 'cerebrus_screener' tool in the tools list returned by list_tools(). Defines name, description, and inputSchema accepting an optional 'top_n' integer parameter.
    Tool(
        name="cerebrus_screener",
        description=(
            "Scan all 30+ coins for top trading signals. Returns RSI zone, trend, "
            "volatility regime, funding bias, multi-TF confluence score with alignment, "
            "and OI trend for each coin. "
            "Much cheaper than calling pulse individually. Cost: $0.04 USDC via x402."
        ),
        inputSchema={
            "type": "object",
            "properties": {
                "top_n": {
                    "type": "integer",
                    "description": "Number of top coins to return (1-100). Default: 30",
                    "default": 30,
                    "minimum": 1,
                    "maximum": 100,
                },
            },
        },
    ),
  • Handler for the 'cerebrus_screener' tool. Extracts optional 'top_n' argument (default 30), then delegates to _api_get() calling the '/screener' endpoint with the top_n parameter.
    elif name == "cerebrus_screener":
        top_n = arguments.get("top_n", 30)
        result = _api_get("/screener", params={"top_n": top_n})
  • Helper function _api_get() that executes all API calls including the '/screener' endpoint. Handles 402 (payment required), 429 (rate limited), and other HTTP errors.
    def _api_get(path: str, params: dict | None = None) -> dict[str, Any]:
        """Make a GET request to the Cerebrus Pulse API."""
        with _make_client() as client:
            resp = client.get(path, params=params)
    
            if resp.status_code == 402:
                # Return payment details so the agent/user knows cost
                return {
                    "status": "payment_required",
                    "message": "This endpoint requires x402 USDC payment on Base or Solana.",
                    "url": f"{BASE_URL}{path}",
                    "payment_details": resp.headers.get("X-Payment", "See x402 SDK docs"),
                    "help": "Install the x402 SDK and set CEREBRUS_WALLET_KEY (Base) or CEREBRUS_WALLET_KEY_SOLANA (Solana) to enable auto-payment. See https://cerebruspulse.xyz/guides/x402-payments",
                }
    
            if resp.status_code == 429:
                return {
                    "status": "rate_limited",
                    "message": "Rate limit exceeded. Back off and retry.",
                    "detail": resp.json() if resp.headers.get("content-type", "").startswith("application/json") else resp.text,
                }
    
            resp.raise_for_status()
            return resp.json()
  • CLI tool mapping for 'screener' in _CLI_TOOLS dict, mapping to the '/screener' API path with an optional top_n parameter defaulting to 30.
    "screener":      ("/screener",          [("top_n", False, int, 30)]),
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must compensate. It discloses the cost ($0.04 USDC) and implies a read operation (scanning signals). However, it does not mention authorization needs, rate limits, or any side effects. The behavioral disclosure is partial but adds some value beyond typical.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loading the action and key differentiators. Every sentence serves a purpose: first sentence states core functionality, second lists return fields and cost. No extraneous text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (single optional parameter, no output schema), the description covers the main behavior and output fields. It lists the return metrics (RSI zone, trend, etc.) to inform the agent. While it could detail the output format more, it is sufficient for selection and basic understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter (top_n) with 100% description coverage via the schema's own description. The tool description does not add additional semantic detail about the parameter; it mentions 'scan all 30+ coins' but the parameter defaults to 30, which is consistent. Baseline 3 is appropriate as schema fully documents the parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool scans all 30+ coins for top trading signals and lists the specific metrics returned (RSI, trend, volatility, etc.). It distinguishes from the sibling 'cerebrus_pulse' by noting it is cheaper for bulk scanning.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description compares directly to cerebrus_pulse, saying it is much cheaper than calling pulse individually. This gives clear guidance on when to use this tool over that specific sibling. However, it does not provide guidance relative to other siblings like cerebrus_basis or cerebrus_funding, nor does it mention when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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