Verify AI agent trust via LimitGuard.
Checks if an AI agent is trusted based on its identifier.
Used for multi-agent systems to verify delegation targets.
Args:
agent_id: Unique agent identifier
agent_name: Human-readable agent name
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | Yes | ||
| agent_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 behavioral burden. It implies a read-only check and names the external trust source (LimitGuard), but it does not clarify what 'trusted' means, how trust is determined, or what failure modes exist. The 'Checks' wording weakly signals non-mutation, but more detail would be valuable.
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 first line and second line are mildly redundant ('Verify AI agent trust' vs 'Checks if an AI agent is trusted'), but overall there is no wasted content and the Args section is clearly organized.
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?
Given the simple tool shape (2 flat params, output schema present), the description covers purpose, usage context, and parameter meaning. It does not discuss trust semantics or alternative tool routing, but these are not critical for a basic delegated-trust check. The presence of an output schema means return values are documented elsewhere.
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 explain the parameters. It does: agent_id is 'Unique agent identifier' and agent_name is 'Human-readable agent name.' These are basic but add meaning beyond the bare schema titles. Slightly more detail on expected formats or validation would improve it.
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?
States a specific verb and resource: 'Verify AI agent trust via LimitGuard' and 'Checks if an AI agent is trusted based on its identifier.' It clearly distinguishes this from sibling tools like check_entity, verify_wallet, and get_trust_score by focusing on agent trust verification for delegation.
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?
Gives a clear context: 'Used for multi-agent systems to verify delegation targets.' It tells an agent when this tool is relevant, though it does not explicitly mention exclusions or alternative sibling tools.
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.