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Verigent-AI

@verigent/mcp-server

Official
by Verigent-AI

report_agent

Flag a counterparty whose actions contradict its verified profile, initiating a community dispute review; passing a new verification clears the flag.

Instructions

Flag a counterparty whose behaviour looks inconsistent with its verified Verigent profile (e.g. a suspected model swap). This raises a public dispute flag on the agent — it does NOT trust the accuser blindly, and it does not let Verigent probe anyone's infrastructure. The community does the checking; Verigent only records the flag. A reported agent clears the flag by passing a fresh verification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
handleYesHandle of the agent you are reporting
reasonYesWhat looks inconsistent (required)
evidenceNoOptional transcript snippet or observation supporting the report
reporterNoOptional: your own handle/contact, for credibility

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.15

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that this raises a public flag, that Verigent only records it, that the community does the checking, and that a reported agent clears the flag by passing fresh verification. This is meaningful behavioral context beyond the schema.

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 compact and front-loaded with the primary action, then clarifies boundaries and consequences. Every sentence adds value, though the final sentence about clearing the flag could be seen as slightly beyond the core usage guidance.

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?

For a tool with no output schema and no annotations, the description covers the action, the consequences, the limitations, and the resolution path. It doesn't describe the return value, but the absence of an output schema lowers the bar. The main gap is not specifying what happens after reporting (e.g., confirmation details), but the overall context is sufficient.

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 four parameters. The description adds context about the overall purpose but doesn't add per-parameter meaning 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 action (flag a counterparty), the target (agent whose behavior looks inconsistent with its verified Verigent profile), and the specific example (suspected model swap). It distinguishes itself from siblings by emphasizing this is a public dispute flag, not a verification or probe action.

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 explains when to use this tool (when behavior looks inconsistent with a verified profile) and what it does NOT do (does not trust accuser blindly, does not probe infrastructure). It doesn't explicitly name alternative tools, but the exclusions help an agent understand the boundary. The community-checking model is clearly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.