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x402 bulk trust scoring (paid)

x402_trust_bulk

Score up to 500 x402 endpoints in a single paid call, returning trust scores, confidence, and recomputation status to assess endpoint reliability before payment.

Instructions

Score up to 500 x402 endpoints in a SINGLE paid call. Returns the authoritative full-density trust score (0-100, grade A-F or '?' when unmeasured, recommendation proceed|caution|avoid|parameterize|unverified|not-payable), confidence, probed_at, computed_at, and a recomputed flag for each requested resource. Cache rows older than ~15 minutes are recomputed on-demand from the latest stored probes and settlements (no live network re-probe), so bulk scores typically reflect reality within minutes. Per-request recompute limits apply: at most 50 endpoints / 8 seconds are recomputed; the response includes recompute_limit_hit and recompute_limit so you know if the cap was reached. The smallest tier that fits your request is selected automatically (10/50/100/200/500 endpoints; ~$0.045/$0.20/$0.325/$0.40/$0.50). Resources not in our observation set return found:false; you still pay for the batch. For a fresh live probe, use x402_trust_score. Pay-per-call over x402; auto-pays if a wallet is configured, otherwise returns the price quote.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierNoOptional fixed tier size. If omitted, the cheapest tier that fits `resources` is used.
resourcesYesList of full x402 resource URLs (https://...) to score. Duplicates are ignored; max 500.
Behavior5/5

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

No annotations are provided, so the description carries full burden. It thoroughly discloses paid nature, cache staleness (~15 min), recompute limits, response flags (recompute_limit_hit), automatic tier selection, pricing, and found:false behavior. This is comprehensive behavioral 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?

Although longer than typical, every sentence adds essential operational detail (purpose, outputs, caching, limits, pricing, alternatives). The structure front-loads the primary purpose and then covers edge cases, making it remarkably information-dense without fluff.

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 the tool has no output schema, the description comprehensively explains the return fields (trust score, grade, recommendation, confidence, timestamps, recomputed flag) and all relevant edge cases (not-found, recompute limits, payment behavior). This makes the tool complete for both selection and invocation.

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?

Schema coverage is 100%, so baseline is 3. The description adds meaningful context beyond schema: automatic tier selection, pricing for each tier, and the implication that resources not in the observation set still cost money. It also clarifies the 'resources' parameter semantics with the found:false behavior, adding value beyond the raw schema.

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 uses a specific verb ('Score') and resource ('x402 endpoints') in a clear scope ('up to 500' in a 'SINGLE paid call'). It distinguishes from siblings explicitly by naming x402_trust_score as the alternative for fresh live probes.

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

Usage Guidelines5/5

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

Provides explicit usage context: suitable for bulk scoring using cached data, with clear guidance that live probing is handled by x402_trust_score. It also warns about paying even for resources not found and explains the per-request recompute limits, helping agents decide when to call this tool.

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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