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Mifori CertAgent

Verify Agent Reputation (x402 Audit)

verify_agent_reputation

Executes a live <500ms outbound HTTP audit against target_bot_url to measure Synthetic IQ (0-100), round-trip latency, JSON schema compliance, and hallucination risk, persisting a signed certificate (eval_id) to the public ledger. Call with target_bot_url alone (omitting payment_proof) for a free, side-effect-free PAYMENT_REQUIRED_402 invoice, then supply a 0x Base Mainnet USDC transaction hash ($0.005) or X-Agent-Key credit in payment_proof to run the live probe. Use before delegating tasks to a specific endpoint; use get_agent_leaderboard instead for free read-only ranking lookups, or submit_agent_feedback to dispute an existing eval_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payment_proofNoOptional 66-character Base Mainnet USDC transaction hash ('0x...') for the $0.005 x402 audit fee or an X-Agent-Key credit token. Omit on initial call to receive the free HTTP 402 payment invoice.
target_bot_urlYesPublicly reachable HTTPS/HTTP endpoint URL of the AI agent to audit (e.g. 'https://agent.mifori.com/v1/demo-agent'). Must accept JSON POST probes.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoError code or payment requirement description if audit was not completed.
statusYesExecution status ('VERIFIED', 'PAYMENT_REQUIRED_402', or 'ERROR').
eval_idNoUnique identifier (UUID) of the audit evaluation.
latency_msNoMeasured round-trip execution latency in milliseconds.
synthetic_iqNoComputed Synthetic IQ score between 0 and 100.
target_bot_urlYesAudited target agent URL.
reliability_tierNoAssigned Mifori trust tier based on IQ and latency.
certagent_signatureNoCryptographic HMAC-SHA256 signature of the audit certificate.
json_schema_compliantNoWhether the target agent returned a valid JSON contract.
hallucination_detectedNoWhether logical hallucination or invalid output was detected.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / properties / latency_ms / type
      Previous value: -"integer"New value: +"number"
  2. Changed2 schema fields changed
    • changedInput schema / properties / payment_proof / description
      Previous value: -"Optional Base Mainnet USDC transaction hash (0x...) or x402 payment proof ($0.005 USDC). If omitted, the tool returns the HTTP 402 payment invoice."New value: +"Optional 66-character Base Mainnet USDC transaction hash ('0x...') for the $0.005 x402 audit fee or an X-Agent-Key credit token. Omit on initial call to receive the free HTTP 402 payment invoice."
    • changedInput schema / properties / target_bot_url / description
      Previous value: -"Valid HTTPS/HTTP URL of the target AI bot endpoint to audit (e.g. https://agent.mifori.com/v1/demo-agent)."New value: +"Publicly reachable HTTPS/HTTP endpoint URL of the AI agent to audit (e.g. 'https://agent.mifori.com/v1/demo-agent'). Must accept JSON POST probes."
  3. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations declare the safety envelope (readOnlyHint=false, destructiveHint=false, openWorldHint=true, non-idempotent), and the description adds the behavior that actually matters: an outbound request to a third-party URL with a <500ms budget, a persistent signed certificate written to a public ledger, an explicit $0.005 fee, and a free side-effect-free 402 invoice when payment_proof is omitted.

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?

Front-loaded with the purpose, then the payment mechanics, then sibling routing, with no filler. The first sentence is long and dense with parentheticals, which costs it a point on readability, but every clause conveys actionable information.

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?

An output schema exists, so return values are already covered. For a networked, paid, non-idempotent tool the description supplies what is otherwise missing: latency expectation, cost, payment flow, ledger persistence, and sibling alternatives. Nothing needed to invoke it correctly is absent.

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% and the schema already documents both the 0x hash format and the X-Agent-Key credit option, so the baseline is 3. The description adds genuine operational meaning beyond the schema by stating that omitting payment_proof yields a free PAYMENT_REQUIRED_402 invoice, which is the sequence the caller must follow.

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?

States a specific verb (executes a live outbound HTTP audit), the resource (target_bot_url / the target agent), and exactly what it measures (Synthetic IQ, latency, JSON schema compliance, hallucination risk) plus the persisted artifact. It also names what it is not by distinguishing itself from get_agent_leaderboard and submit_agent_feedback.

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?

Gives explicit when-to-use ('before delegating tasks to a specific endpoint') and two named alternatives with their own scoping conditions: get_agent_leaderboard for free read-only ranking and submit_agent_feedback for disputing an eval_id. The two-phase call flow (omit payment_proof first, then supply proof) is spelled out directly.

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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