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The Mirror System — AI Citability

by com.danielsdesignstudio

The ACI 55 (published index lookup)

aci55
Read-onlyIdempotent

Look up The ACI 55 — Daniels AI's published benchmark of 55 leading consumer brands measured by AI Citability Score (ACS), the standard for Brand Discovery Intelligence (BDI) across answer engines (AEO), generative AI (GEO), and search (SEO). Presented by category as a banded dataset (bands: Above Average / Average / Below Average / Excellent) — NOT a 1-to-55 ranking. Call with NO arguments to get the complete dataset: 11 categories, each brand with brand, category, ACS band, ACS, AEO, GEO, and SEO. Call with a brand name for that brand's ACI 55 standing — its band, ACS, sub-scores, category, priority gap, and summary. This is the published, citable standard (August 2026 Edition · Rubric V3.0, CC BY 4.0). For a brand not in the index, or for a full audit with prioritized findings and fixes, use the reflect tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNoOptional brand name to look up (e.g. 'Ford', 'Nike'). Omit to return the complete banded dataset — all 55 brands, by category.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive. The description adds valuable context: the dataset is banded, not a 1-to-55 ranking, and enumerates return fields. No contradiction; minor omissions like error handling are acceptable for a simple lookup.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with purpose front-loaded, followed by usage modes and alternatives. Though somewhat verbose, each sentence contributes meaningful information. Minor redundancy in licensing details could be trimmed.

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

Completeness4/5

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

No output schema, so the description covers return contents for both invocation modes. It also mentions the alternative tool for edge cases. Minor gap: doesn't specify what happens if a brand is not found in the index, but overall complete for a read-only lookup.

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 schema already describes the optional `brand` parameter well, and the description reinforces this by explaining the behavior of omitting vs. providing the brand, including example values and expected result fields. Goes slightly beyond schema.

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 tool's purpose: 'Look up The ACI 55' — a specific benchmark dataset. It uses a specific verb+resource and distinguishes itself from siblings by naming the `reflect` tool for alternative use cases.

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?

Explicitly explains when to call with no arguments (complete dataset) versus with a `brand` name (specific standing), and directs users to `reflect` for brands not in the index or full audits. This provides clear decision guidance.

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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TDQS

A4.1/5.0
Disambiguation4/5

Each tool targets a distinct function: benchmark lookup, engine measurement, full audit, record request, quick score, and studio output. Some overlap occurs between 'score' and 'reflect' (both assess citability), but their scope difference (lite vs. full) is clearly delineated in descriptions.

Naming Consistency2/5

Tool names are inconsistent: 'aci55' uses an acronym and number, 'mcp_engine' uses lowercase with underscore, 'reflect' is a single verb, 'request_record' is verb_noun, 'score' is a single noun, and 'studio' is a single noun. No predictable pattern emerges.

Tool Count4/5

With 6 tools, the count is well-scoped for a niche domain like AI citability measurement. Each tool feels justified, covering lookup, scoring, audit, record creation, and output generation.

Completeness4/5

The tool set covers the core lifecycle: quick estimate (score), full audit (reflect), benchmark (aci55), MCP presence check (mcp_engine), record request (request_record), and output generation (studio). Minor gap: no tool for editing or deleting records directly, but that fits the human-approval workflow described.

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