Agent Reliability

Overview of Agent Reliability

get_overview

Read-onlyIdempotent

Corpus overview: what this instance knows, counts by type, published tags, freshness. Use this first when you land here and do not yet know whether this corpus can answer your question.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_overview"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`tags`	Yes
`by_type`	Yes
`instance`	Yes
`description`	Yes
`total_media`	Yes
`total_objects`	Yes
`newest_verification`	Yes
`oldest_verification`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 1, 2026

addedOutput schema / properties / total_media

Added value: +{
+  "maximum": 9007199254740991,
+  "minimum": -9007199254740991,
+  "type": "integer"
+}

changedOutput schema / required

Previous value: -[
-  "instance",
-  "description",
-  "total_objects",
-  "by_type",
-  "tags",
-  "newest_verification",
-  "oldest_verification"
-]New value: +[
+  "instance",
+  "description",
+  "total_objects",
+  "by_type",
+  "tags",
+  "newest_verification",
+  "oldest_verification",
+  "total_media"
+]

First observedAug 23, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context about the tool's role as a preliminary orientation step and discloses the nature of its outputs (counts, tags, freshness), which goes beyond the annotations without contradicting them.

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?

Two sentences, no fluff. The first sentence front-loads the tool's content and scope, and the second immediately gives actionable usage guidance. Every word earns its place.

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 parameterless overview tool with a rich output schema and safety-oriented annotations, the description fully covers purpose, usage, and context. Nothing essential is missing for an agent to decide when to invoke it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so parameter semantics are not applicable. Per the rubric, a 0-parameter tool receives a baseline of 4; the description appropriately focuses on the tool's output rather than parameters.

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 clearly states the resource ('Corpus overview') and the specific content returned: counts by type, published tags, freshness. It distinguishes itself from siblings by positioning this as the first-stop tool for assessing corpus knowledge, which separates it from query tools like answer or search.

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

Usage Guidelines4/5

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

It gives explicit when-to-use guidance: 'Use this first when you land here and do not yet know whether this corpus can answer your question.' It implies when not to use it (when you already know the corpus can answer), but it does not explicitly name alternatives or exclusion conditions, so it falls just short of a 5.

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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