Quick risk assessment without full trust check.
Faster endpoint that focuses on risk signals only.
Use when you only need basic risk evaluation.
Args:
entity_name: Full legal name of the entity
country: ISO 3166-1 alpha-2 country code
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes | ||
| entity_name | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It reveals that the tool is faster and limited to risk signals, but it does not state whether the operation is read-only, what the response contains, whether there are rate limits, or any error conditions. This lack of operational detail is a notable gap for a tool with zero annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the core purpose. The only waste is minor redundancy between 'Quick risk assessment without full trust check' and 'Faster endpoint that focuses on risk signals only,' which restate the same idea. The Args section is clean and useful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The essential invocation details are present: when to use, both parameter meanings, and the lightweight scope. An output schema exists, so not explaining return values is acceptable. However, the description remains thin on behavioral context and does not explicitly route the agent to get_trust_score for full trust checks, so it is adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but the description compensates by defining both parameters: entity_name as 'Full legal name of the entity' and country as 'ISO 3166-1 alpha-2 country code.' This adds critical meaning beyond the bare schema titles and gives the agent what it needs to supply valid inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('risk assessment') and resource ('risk score'), and differentiates itself from a full trust check: 'Quick risk assessment without full trust check.' It also positions itself as a faster endpoint focused only on risk signals, which clearly distinguishes it from the sibling get_trust_score and aligns with the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description says 'Use when you only need basic risk evaluation,' providing a clear use condition. However, it does not explicitly say when not to use it or name an alternative tool for full trust checks, relying on the implied contrast with 'full trust check.' This is solid guidance but not fully explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.