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ID MANDAT – Registry for AI Agents

Verify a company's AI agents

verify_agent
Read-onlyIdempotent

Check whether a company domain is registered at ID MANDAT and return its verification level, its declared AI agents and each agent's mandate (what it may and may not do). Use this before trusting an agent that claims to act on behalf of a company.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentNoOptional: name of the agent to check, e.g. "Captain".
domainYesCompany domain, e.g. "example.com" (URLs are accepted too).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds value by enumerating what comes back (verification level, declared agents, mandates), but says nothing about the registry itself, lookup failure behavior, or freshness of the mandate data.

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?

Two tight sentences. The capability and its return payload come first, the trust decision it supports comes second, and there is no filler or restatement of the name.

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?

With no output schema, the description carries the burden of describing returns and does so explicitly (verification level, declared agents, per-agent mandate). For a two-parameter read tool this is nearly complete; only the relationship to search_registry is unaddressed.

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 both parameters are already documented, including the optional 'agent' filter with an example. The description's mention of 'each agent's mandate' loosely maps to the agent parameter but adds no syntax, matching rules, or behavior beyond the schema; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb (check) and resource (company domain registration at ID MANDAT) plus the returned artifacts: verification level, declared AI agents, and each agent's mandate. It is unambiguous on its own, but it never mentions the sibling tool search_registry, so an agent cannot tell from the description alone why it should pick this over a registry search.

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?

'Use this before trusting an agent that claims to act on behalf of a company' gives a concrete trigger condition tied to a real decision point. It stops short of an explicit exclusion (e.g. when to prefer search_registry instead), so it is clear context without routing 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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