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Agent Reliability

by dev.agentreliability
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Latest verified Agent Reliability objects

get_latest
Read-onlyIdempotent

Most recently verified knowledge objects (freshness signal). Use this to judge how current the corpus is, or to see what changed since you last read it. It ranks by verification date and ignores your topic entirely — use search or get_topic when you want objects that are relevant rather than recent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many recently verified objects to return, newest verification first.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYes
resultsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedOutput schema / properties / results / items / properties / x
      Added value: +{
+  "additionalProperties": {
+    "additionalProperties": {},
+    "propertyNames": {
+      "type": "string"
+    },
+    "type": "object"
+  },
+  "description": "Indexed instance-specific attributes, grouped by namespace — the same fields api/index.json publishes. Absent when the instance declares none.",
+  "propertyNames": {
+    "type": "string"
+  },
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / properties / limit / description
      Added value: +"How many recently verified objects to return, newest verification first."
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive behavior; the description adds useful behavioral context beyond them by stating that ranking is by verification date and that topic is deliberately ignored. It does not clutter with unnecessary return details because an output schema exists.

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?

Three sentences, each earning its place: the core definition is front-loaded, the practical use cases follow, and the distinction from search/get_topic closes it. There is no filler or repetition.

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?

For a single-optional-parameter read-only tool with an output schema, the description fully equips an agent: what is returned, what ordering is used, when to use it, and which sibling tools to prefer instead. Nothing critical 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?

The only parameter (limit) is fully documented in the schema with default, range, and ordering semantics ('newest verification first'), so the description carries no additional parameter burden. Per the high schema-coverage baseline, a 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete resource ('knowledge objects'), specifies the ordering ('most recently verified'), and immediately distinguishes itself from relevance-based siblings by noting 'it ignores your topic entirely'. This lets an agent know exactly what get_latest returns and how it differs from search/get_topic.

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

Usage Guidelines5/5

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

It gives explicit use cases ('judge how current the corpus is' or 'see what changed since you last read it') and an explicit exclusion/alternative: use search or get_topic when topical relevance, not recency, is desired. No ambiguity remains about when to choose this tool.

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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