The Mirror System — AI Citability

Request a Brand Discovery Record

request_record

Request a Brand Discovery Record — the human-approved source of truth a brand provides to AI assistants and agents, maintained by Mirror. A Record is a machine-readable reference page, hosted by Mirror, stating a brand's verified facts with schema.org structured data and citations, and linking to the brand's own site as the canonical source. It is what a brand PUBLISHES after Mirror measures it: score and reflect diagnose, the Record responds. IMPORTANT — this tool creates a REQUEST, not a Record. Nothing is published by calling it. Automated validation (Gate 1) runs immediately and its results are returned to you. Every Brand Discovery Record is reviewed by Daniels AI and approved by the organization it represents before publication. No autonomous process publishes a brand's machine-readable truth. Fill the fields below with verified, citable facts only. The Record specification is versioned; this tool reports the version it validated against.

Input Schema

TableJSON Schema

Name	Required	Description
`social`	No	Official profile URLs.
`address`	No	{street, city, region, postal, country}
`history`	No	Founding and lineage, factual.
`tagline`	Yes	One plain line: what it is and where.
`overview`	No	Short factual prose — what the brand is.
`citations`	Yes	At least 3 authoritative sources from 3 DIFFERENT domains.
`brand_name`	Yes	Exact public name of the brand.
`phone_e164`	No	E.164 format, e.g. +1-802-760-4653
`description`	Yes	1-2 factual sentences, ~160 characters. No marketing claims.
`positioning`	No	The most clarifying fact: category, who it serves, and what it is NOT.
`recognition`	No	Awards or coverage, each traceable to a citation.
`schema_type`	Yes	schema.org @type — e.g. GolfCourse, Restaurant, LodgingBusiness, Organization, LocalBusiness.
`official_url`	Yes	The brand's OWN official website (canonical). Must be the brand's domain — never Mirror's.
`relationship`	No	Who is asking, and on whose behalf. Only a request from someone representing the brand can become an authorized Record.
`founding_year`	No	Year established (YYYY).
`public_access`	No	Is it open to the public? The confusion-killer field.
`differentiator`	No	The notable, verifiable thing.
`requester_note`	No	Why this request is being made.
`requester_contact`	No	Email for Gate 2 follow-up. Without it a Record cannot be authorized.

TDQS

A4.3/5.0

Behavior5/5

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

The description adds significant behavioral context beyond annotations: it confirms the tool is a request, not a publication, runs immediate validation (Gate 1), and requires human approval. No contradictions with annotations.

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?

The description is verbose but well-structured: purpose first, then context, then warnings. Every sentence adds value, though it could be more concise without losing clarity.

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?

For a tool with 19 parameters, nested objects, and no output schema, the description explains the lifecycle, validation, and human approval process. It mentions that validation results are returned but does not detail the response format, which is a minor gap.

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?

Schema description coverage is 100%, so the schema already documents each parameter. The description adds no parameter-specific details, but provides overall context for the request process. Baseline 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 clearly states the tool creates a REQUEST, not a Record, and explains that it is part of a lifecycle with Mirror. It distinguishes itself from siblings like 'score' and 'reflect' by describing the tool's role in the pipeline.

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 description explicitly warns that no publication occurs upon calling this tool, and explains the human review process. It provides context on when to use this tool via the lifecycle narrative, but does not explicitly name alternatives or give when-not-to-use 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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