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ID MANDAT – Registry for AI Agents

Search the ID MANDAT registry

search_registry
Read-onlyIdempotent

Search registered companies by name, industry, city, offer or agent capability (e.g. "book_appointment"), optionally filtered by country. Returns up to 20 entries, best verified first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoFree text, e.g. "art gallery", "Osnabrück", "book_appointment".
countryNoOptional ISO 3166-1 alpha-2 country code, e.g. "DE", "AT", "CH".
with_agentNoOptional: only companies that declare at least one AI agent.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so safety is covered. The description adds real behavioral context beyond them: a result cap of 20 entries and a 'best verified first' ordering, which tells the agent how to interpret and trust the output.

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 dense sentences with zero filler; the searchable dimensions and the country filter are front-loaded before the return/ordering note. Every clause carries information.

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 correctly supplies the return shape (up to 20 entries, ranked by verification status). It omits pagination or behavior when more than 20 matches exist, a minor gap for a search tool.

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 coverage is 100%, so all three parameters are already documented with types and examples. The description restates the same field list and repeats the book_appointment example rather than adding new semantics such as matching behavior or precedence, so the 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?

States a specific verb and resource ('Search registered companies') and enumerates the fields the query matches plus the country filter, so the agent knows exactly what this tool retrieves. It does not name the sibling verify_agent or draw the discovery-vs-verification line, so it falls short of full sibling differentiation.

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

Usage Guidelines3/5

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

Usage is implied by the wording ('search ... optionally filtered by country'), which suggests a discovery/browse role, but there is no explicit when-to-use statement and no mention of verify_agent as the alternative. The agent must infer the routing from the sibling's name alone.

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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