LimitGuard Trust Intelligence

check_entity

Check entity trust score via LimitGuard API.

    Full trust intelligence check on a business entity.
    Returns trust score (0-100), risk level, and recommendation.

    Args:
        entity_name: Full legal name of the entity
        country: ISO 3166-1 alpha-2 country code (e.g., NL, BE, DE)
        kvk_number: Optional Dutch KVK registration number (8 digits)
        domain: Optional company website domain

Input Schema

TableJSON Schema

Name	Required	Description	Default
`domain`	No
`country`	Yes
`kvk_number`	No
`entity_name`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedMar 2, 2026

TDQS

A3.5/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose the main behavioral outcome: returns a trust score (0-100), risk level, and recommendation. However, it does not mention authorization needs, rate limits, whether any external side effects occur, or failure behavior. It is more transparent than a bare mutation tool, but still incomplete for a no-annotation case.

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 well-structured and efficient, with a one-line purpose, a brief output summary, and a clear Args section. It is slightly redundant between the opening line and 'Full trust intelligence check,' but overall every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema and a solid parameter description, the tool is fairly complete for invoking it. However, with four sibling tools in the same domain and no usage guidance or differentiation, the description is not fully complete for an agent deciding which tool to select. The gap is mostly in routing and alternatives rather than parameter understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully explain the parameters, and it does. Each argument is described concisely: entity_name is the full legal name, country uses ISO 3166-1 alpha-2, kvk_number is an optional 8-digit Dutch registration, and domain is optional. This adds meaningful semantic detail the schema lacks.

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 states a specific verb and resource: 'Check entity trust score via LimitGuard API' and expands with 'Full trust intelligence check on a business entity.' It clearly identifies what the tool does and what it returns, but it does not explicitly differentiate itself from sibling tools such as get_trust_score or get_risk_score.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of check_agent, get_trust_score, get_risk_score, or how the full check relates to them. The usage context is only implied by 'Full trust intelligence check,' but no when-to-use or when-not-to-use instructions are given.

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

All LimitGuard Trust Intelligence tools Inspect

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TDQS

A3.9/5.0

Disambiguation4/5

Most tools have distinct purposes: check_agent for AI agents, check_entity for full business checks, verify_wallet for crypto addresses. However, get_risk_score and get_trust_score overlap conceptually with check_entity's outputs, potentially causing confusion about when to use each. The descriptions help differentiate them as quick vs. full checks, but the boundaries could be clearer.

Naming Consistency5/5

All tools follow a consistent verb_noun naming pattern (check_agent, check_entity, get_risk_score, get_trust_score, verify_wallet). The verbs 'check', 'get', and 'verify' are semantically appropriate for their functions, and the snake_case style is uniformly applied throughout the set.

Tool Count5/5

With 5 tools, this server is well-scoped for its trust intelligence domain. Each tool serves a specific function (agent verification, entity checks, risk/trust lookups, wallet verification), and none feel redundant or out of place. The count is appropriate for covering key aspects of trust assessment without being overwhelming.

Completeness4/5

The tool set covers major trust intelligence use cases: agent verification, business entity checks (full and quick variants), and crypto wallet verification. A minor gap is the lack of tools for updating or managing trust data (e.g., reporting false positives), but for a read-only API surface, it provides good coverage of core query operations.

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