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aggregate_audit_results

Combine multiple audit results into one aggregated summary. Group issues by URL, category, rule, or none, and include summary statistics.

Instructions

Combine and aggregate multiple audit results. Groups issues by URL, category, rule, or none, and provides aggregated summary statistics.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "results": "<results>"
    },
    "name": "aggregate_audit_results"
  }
}

Input Schema

Name	Required	Description	Default
`groupBy`	No	Grouping strategy: "url" (group by URL), "category" (group by category), "rule" (group by rule ID), or "none" (no grouping). Default: "url".	url
`results`	Yes	Array of audit result objects to aggregate.
`includeSummary`	No	Include aggregated summary statistics (default: true).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.13Aug 6, 2026

TDQS

A3.7/5.0

Behavior3/5

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

No annotations are provided, so the description carries the transparency burden. It discloses the grouping strategies (url, category, rule, none) and that aggregated summary statistics are produced, but it does not explain the output format or whether the input results are modified. This is moderate behavioral disclosure but leaves important details unspecified.

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?

The description is a single sentence that leads with the main action, then lists grouping options and the summary output. Every phrase adds value; no redundancy or unnecessary detail.

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

Completeness3/5

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

The schema clarifies parameters, but there is no output schema, so the description should describe return values. It mentions 'aggregated summary statistics' only vaguely and does not specify the shape of the returned object, which is a meaningful gap for a tool that aggregates heterogeneous audit results.

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 schema describes all three parameters with 100% coverage, so the description does not need to repeat them. The description's mention of grouping and summary statistics mirrors the schema's enum and includeSummary field without adding deeper semantics or usage nuance, meeting the baseline.

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?

The description uses a specific verb-resource pair: 'Combine and aggregate multiple audit results.' It also names the grouping options, which clarifies its role. However, it does not explicitly differentiate itself from siblings like get_statistics or generate_summary_report, so it is clear but not fully distinctive.

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?

The phrase 'multiple audit results' indicates the intended context: use when you have several audit result objects to aggregate. It does not provide explicit exclusions or name alternative tools for different scenarios, but the condition is clear enough for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.