LION Trend Intent MCP

lion_trend_intent_signal_json

Hardware-wallet buyer-intent + self-custody onboarding-friction intelligence as JSON. Non-PII public-signal feed covering: Ledger vs Trezor comparison demand, Bitcoin-only wallet research, under-$100 wallet shopper queries, DeFi hardware-wallet intent, and adjacent decision-stage crypto purchase research. Designed for agent routing, affiliate / comparison-content workflows, and research context. Pay $0.01 USDC on Base mainnet via x402 (HTTP 402 + EIP-3009 transferWithAuthorization) at the paid route. This MCP tool does NOT return the dataset; tools/call returns payment-required metadata so an x402-capable client can settle and fetch the JSON directly.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "lion_trend_intent_signal_json"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedMay 24, 2026

TDQS

A4.4/5.0

Behavior5/5

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

With no annotations, the description fully discloses the critical behavioral aspects: it charges $0.01 USDC via x402, returns payment-required metadata rather than the actual dataset, and requires an x402-capable client to settle and fetch the JSON. It also notes the feed is Non-PII, offering strong transparency.

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 uses five focused sentences, each adding distinct value: content taxonomy, use cases, payment mechanism, and return behavior. It is front-loaded and contains no filler or redundant statements.

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

Completeness5/5

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

Given no parameters or output schema, the description fully explains the dataset scope, intended workflows, payment/settlement process, and the fact that the MCP call returns metadata rather than data. This is sufficient for an agent to correctly invoke and handle the tool.

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

Parameters4/5

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

The tool has zero parameters and the input schema is empty, so the schema provides complete coverage. The description adds no parameter details, but none are needed; the baseline of 4 for zero-parameter tools applies.

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

Purpose4/5

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

The description clearly identifies the tool as a hardware-wallet buyer-intent signal feed in JSON format and lists specific covered topics, distinguishing it from the CSV sibling by format and content. However, it lacks an explicit action verb like 'retrieve' or 'provide,' making the purpose slightly implicit rather than directly stated.

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 states clear use contexts: agent routing, affiliate/comparison-content workflows, and research. It does not explicitly name when not to use this tool versus the CSV alternative, but the provided use cases give enough guidance for selection.

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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