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AgentTrust — Pre-Invocation AI Agent Trust & Reliability Checks

Get Agent Health

get_agent_health
Read-onlyIdempotent

Get one agent's current effective health status and monitoring detail — derived from heartbeat freshness for push-mode agents, or the latest pull check otherwise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesThe agent's URL slug (from its public profile or a list_agents result).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes
statusYes
agentIdYes
latencyMsYes
httpStatusYes
checkStatusYes
lastCheckedAtYes
reliabilityScoreYes
reliabilityScoreStatusYes
reliabilityScoreComputedAtYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedOutput schema / properties / reliabilityScoreStatus
      Added value: +{
      +  "enum": [
      +    "none",
      +    "fresh",
      +    "stale"
      +  ],
      +  "type": "string"
      +}
    • changedOutput schema / required
      Previous value: -[
      -  "agentId",
      -  "slug",
      -  "status",
      -  "lastCheckedAt",
      -  "latencyMs",
      -  "httpStatus",
      -  "checkStatus",
      -  "reliabilityScore",
      -  "reliabilityScoreComputedAt"
      -]New value: +[
      +  "agentId",
      +  "slug",
      +  "status",
      +  "lastCheckedAt",
      +  "latencyMs",
      +  "httpStatus",
      +  "checkStatus",
      +  "reliabilityScore",
      +  "reliabilityScoreStatus",
      +  "reliabilityScoreComputedAt"
      +]
  2. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description goes beyond them by explaining how the status is derived — heartbeat freshness for push-mode agents, latest pull check otherwise — and that the result is an 'effective' (computed) status rather than a raw reading. It still omits behavior for unknown slugs or agents that have never reported.

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?

One sentence, front-loaded with the action and resource, with the derivation rule attached as a qualifier rather than padding. Nothing here is wasted.

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

Completeness5/5

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

With an output schema present, the description needn't explain return fields; it only has to identify the resource and the one input, which it does. The push/pull derivation note supplies the extra context a caller needs for a health tool, so nothing material is missing.

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?

There is a single parameter at 100% schema description coverage, so the schema already documents the slug, its source and its length bounds. The description adds no further meaning about the parameter, which matches the baseline of 3 when the schema does the heavy lifting.

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 ('Get one agent's current effective health status and monitoring detail') and clarifies that the value is computed rather than stored. It does not explicitly distinguish itself from the sibling get_agent, but the 'health status' framing is distinct enough that an agent can pick it out of the sibling list.

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 — reach for this when you need an agent's health — and the sentence about push-mode vs pull-mode derivation hints at which internal data source feeds the result. There is no explicit when-to-use/when-not statement or named alternative (e.g. 'for raw config use get_agent'), so guidance stays at the implied level.

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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