Quick trust score lookup by entity ID.
Fast lookup for previously checked entities.
Returns cached score if available.
Args:
entity_id: Entity identifier (KVK number, domain, or hash)
| Name | Required | Description | Default |
|---|---|---|---|
| entity_id | 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 behavioral disclosure burden. It does disclose that the tool returns a cached score and is intended for previously checked entities, but it does not explain what happens on a cache miss, whether a live lookup can occur, or any error behavior.
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 main point is front-loaded in the first sentence, and the Args section is compact. There is minor redundancy between 'Quick' and 'Fast lookup', but overall the description is appropriately sized and easy to scan.
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?
For a simple one-parameter lookup tool with an output schema, the description covers the entity identifier semantics, the cached nature, and the intended context of previously checked entities. The main gap is the lack of explicit fallback behavior or when to choose a sibling tool instead.
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%, so the description must compensate for the single parameter. It fully does so: 'entity_id: Entity identifier (KVK number, domain, or hash)' gives concrete allowed formats beyond the bare string type in the schema.
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 clearly states a specific action and resource: 'Quick trust score lookup by entity ID.' It further narrows the scope by saying it is a 'Fast lookup for previously checked entities' that returns a cached score, which distinguishes it from full entity checks and the sibling risk/wallet tools.
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 implies when to use this tool: for quick lookups of previously checked entities where a cached score may exist. However, it does not explicitly name alternatives or state when not to use it, such as when a fresh score or full entity check is needed.
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.