LION Trend Intent MCP

lion_payment_attempt_readiness_audit

Pre-spend payment-term trust check for x402 endpoints. Before your agent signs a Payment-Signature, call this with a target x402/MCP service URL: LION extracts the target live 402 payment terms (payTo, amount, asset, network, scheme), cross-checks them for drift across public discovery surfaces, and returns payment_attempt_blockers, buyer_policy_fit, canonical_purchase_recipe, funnel_stage, a transparent readiness score, and risk_flags so the agent can decide whether to sign. 0.05 USDC on Base eip155:8453. Unpaid tools/call returns JSON-RPC error -32402 with the x402 paymentRequirement; wallet-capable MCP clients (e.g. Coinbase Payments MCP, AWS AgentCore Payments) auto-construct the EIP-3009 signature. LION does NOT perform payment inside MCP. Checks payment-term consistency only - not a smart-contract security audit, no settlement guarantee, no fund movement.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes	Public HTTPS x402/MCP service URL to audit.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`asset`	Yes
`payTo`	Yes
`amount`	Yes
`scheme`	Yes
`status`	Yes
`network`	Yes
`price_usdc`	Yes
`instructions`	Yes
`paid_endpoint_url`	Yes
`external_paid_proof`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

Describes key behaviors: no payment inside MCP, cost (0.05 USDC), error code (-32402), and return values. Without annotations, it covers safety (read-only check) and scope. Lacks rate limit or auth info but sufficient for one-param tool.

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

Conciseness4/5

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

Description is informative but slightly verbose; however, every sentence adds value. Front-loaded with purpose. Could be more concise but not wasteful.

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 one parameter and presence of output schema, description covers usage, behavior, return values, and constraints. No gaps for effective agent invocation.

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

Parameters5/5

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

Schema covers the single parameter 'url' with description and format. Tool description reinforces its purpose ('target x402/MCP service URL'). Schema coverage is 100%, and description adds no confusion.

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?

Description clearly states the tool is a 'pre-spend payment-term trust check for x402 endpoints' and specifies what it returns (blockers, policy fit, recipe, etc.). It is distinct from sibling tools which are data queries and signals.

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?

Explicitly states when to use ('before your agent signs a Payment-Signature') and notes limitations ('not a smart-contract security audit, no settlement guarantee'). Does not explicitly mention alternatives but siblings are unrelated.

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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TDQS

A4.1/5.0

Disambiguation2/5

The adaptive_data_query tool overlaps with base_dex_signals_json by offering DEX data, and the two trend_intent_signal tools differ only in format (CSV vs JSON), causing ambiguity about which tool to use. The payment audit tool is distinct, but overall the boundaries are unclear.

Naming Consistency4/5

All tools use a consistent 'lion_' prefix and snake_case. However, some tools specify the output format (e.g., '_json', '_csv') while others do not (e.g., 'adaptive_data_query'), creating minor inconsistency. Overall, the pattern is predictable.

Tool Count5/5

With 5 tools covering data queries, DEX signals, payment readiness, and trend signals, the count is well-scoped for the server's purpose. Each tool serves a clear function without excessive redundancy in number.

Completeness4/5

The tool set covers the main use cases: general on-chain data querying, dedicated DEX signals, payment term audits, and trend intent signals. The redundancy between adaptive_data_query and base_dex_signals_json, and the two trend signal formats, is minor. No major gaps are apparent.

Resources

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