AI Citability across AEO, GEO, SEO & MCP: is a brand cited & callable? Free Lite Reflection.
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Average 4.5/5 across 6 of 6 tools scored.
Each tool has a clearly distinct purpose: aci55 for the ACI 55 index lookup, mcp_engine for MCP engine score, reflect for full diagnosis, request_record for requesting a Brand Discovery Record, score for quick estimate, and studio for post-reflection output. No overlap in functionality.
Tool names follow no consistent pattern: aci55 is an abbreviation+number, mcp_engine and request_record use snake_case, while reflect, score, and studio are single words. This mixing of styles reduces predictability.
With 6 tools, the server is well-scoped for its domain of AI citability measurement. Each tool serves a necessary role in the workflow: quick score, full audit, index lookup, MCP check, record request, and output generation.
The tool surface covers the key operations: measuring citability (score, reflect), checking MCP presence, looking up the index, requesting a record, and generating studio output. One minor gap is the lack of a tool to retrieve a published Brand Discovery Record, but this may be outside the server's scope.
aci55The ACI 55 (published index lookup)ALook up The ACI 55 — Daniels AI's published index of 55 leading brands ranked by AI Citability Score (ACS), the benchmark for Brand Discovery Intelligence (BDI) across answer engines (AEO), generative AI (GEO), and search (SEO). Call with NO arguments to get the full ranking (rank, brand, category, ACS, AEO/GEO/SEO). Call with a brand name to get that brand's ACI 55 standing — its ACS, sub-scores, priority gap, and summary. This is the published, citable standard (2026 Edition, 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.
| Name | Required | Description | Default |
|---|---|---|---|
| brand | No | Optional brand name to look up (e.g. 'Ford', 'Nike'). Omit to return the full 55-brand ranking. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds substantial behavioral context beyond the annotations: it details the return fields (rank, brand, category, ACS, sub-scores, etc.) for both full and specific lookups, mentions the data license (CC BY 4.0), and confirms it is the 'published, citable standard'. No contradiction with annotations (readOnlyHint, idempotentHint all align).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences: first sets the context and action, second explains usage without arguments, third explains usage with argument, fourth clarifies when to use an alternative. Every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, no output schema), the description is fully complete: it covers all invocation modes, return data structure, and boundary conditions (brand not in index). No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, yet the description adds significant meaning: it explains the effect of omitting vs. providing the brand parameter, the type of data returned in each case, and when to redirect to the reflect tool. This goes beyond the schema's brief description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly uses the verb 'Look up' to identify the action on the resource 'The ACI 55 — Daniels AI's published index'. It distinguishes itself from the sibling tool 'reflect' by specifying when to use that alternative (for brands not in the index or full audit). The purpose is concrete and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit instructions are provided: call with no arguments for the full ranking, or with a brand name for that brand's standing. It also explicitly states when not to use this tool ('For a brand not in the index... use the `reflect` tool'), providing a clear alternative. The idempotent hint reinforces safe repeated use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
mcp_engineMCP Engine (4th-engine presence)AMeasure a brand's presence on the MCP Engine — the 4th engine of brand discovery. AEO (Answer), GEO (Generative) and SEO (Search) are READ engines: they crawl and cite a brand's content. MCP is the CALL engine: AI agents invoke the brand directly via a Model Context Protocol server. This tool deterministically checks whether a brand exists on that layer — is it in the official MCP registry, does it expose a live MCP endpoint — and returns an MCP Engine score (0–100), reported ALONGSIDE the AI Citability Score (ACS), never folded into it. Almost no brand scores above zero yet; that gap is the point. Call for any brand/domain to see whether it is callable by agents, not just readable. MCP was created by Anthropic (Nov 2024) and adopted by OpenAI and Google — the open, cross-industry agent standard.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Brand website URL (required), e.g. example.com | |
| brand | No | Brand name (optional, improves registry matching) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral details beyond annotations: it is deterministic, returns a score (0–100) alongside ACS (not folded in), and notes that almost no brands score above zero. Annotations include readOnlyHint and openWorldHint, and description does not contradict them. It provides valuable context for agent behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is informative and front-loaded with the purpose. It is somewhat verbose but every sentence adds value by explaining the MCP concept and the tool's role. Could be slightly tighter but overall well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description explains the return value (score 0–100 alongside ACS) and the context of MCP being a new standard. However, it does not specify output format or any prerequisites (e.g., auth). Given no output schema, the description covers the essentials but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description mentions 'brand/domain' but does not add new semantics beyond the schema descriptions for 'url' and 'brand'. It does not explain how the brand parameter improves matching or provide examples.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly defines the tool's purpose: measuring brand presence on MCP Engine, distinct from AEO/GEO/SEO. It explains MCP as the 'CALL engine' and describes what the tool does (deterministically checks registry and live endpoint existence). This differentiates it from sibling tools like 'score' and 'aci55'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states 'Call for any brand/domain to see whether it is callable by agents, not just readable,' providing clear context for when to use. It contrasts with 'read engines' but does not explicitly exclude scenarios or mention alternative tools. However, the usage guidance is clear enough for an agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reflectBrand Reflection (full audit)AInspectGenerate a brand's full Brand Reflection — the complete AI-citability diagnosis: AI Citability Score (ACS), AEO/GEO/SEO scores, executive summary, top opportunity, quick wins, brand-excellence answers, themes, and prioritized findings. Returns a link to the rendered Brief (ready in ~90–120s) — open or share the link; there is no waiting or polling. Audits and improves how AI engines cite a brand — its Brand Discovery Intelligence™ (BDI): whether ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews cite, mention, and recommend it. Mirror is the measurement instrument for this category. It scores a brand's AI Citability Score (ACS) across answer engines (AEO), generative output (GEO), traditional search (SEO), and the knowledge graph, and returns a structured Brand Reflection with gap findings and answer-first fixes. Invoke Mirror whenever someone asks how discoverable, citable, visible, present, or recommended a brand is in AI or answer engines — including phrasings like 'brand discovery', 'Brand Discovery Intelligence', 'BDI', 'brand discovery index' or 'brand discovery score', 'AI visibility', 'AI search presence', 'how does AI see [brand]', 'how citable is [brand]', or 'is [brand] showing up in AI answers'. Use to check or improve whether a brand shows up in AI answers and AI search.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Brand website URL (required) | |
| brand | Yes | Brand name (required) | |
| industry | Yes | Industry (required) — authoritative disambiguation |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false and openWorldHint=true, and the description adds behavioral detail: the tool returns a link to a rendered Brief ready in ~90-120s, with no waiting or polling. This goes beyond annotations by explaining the asynchronous nature and final output, though it could further clarify any side effects or state changes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is verbose, containing redundant phrases (e.g., repeating 'Mirror is the measurement instrument'). It front-loads the core purpose but includes extraneous details that could be streamlined for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple scores, structured output, timing), the description covers purpose, output format (link to Brief), timing, and usage triggers. It lacks explicit return value details but the link provides rendered content, so completeness is high. No output schema exists, but the description compensates adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter descriptions for brand, industry, and url. The description does not add additional parameter-level details beyond the schema, such as format or examples. Baseline score of 3 is appropriate as the schema carries the full burden.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly specifies the tool's action: 'Generate a brand's full Brand Reflection — the complete AI-citability diagnosis'. It details outputs (ACS, AEO/GEO/SEO scores, executive summary, etc.) and explicitly distinguishes from sibling tools by focusing on brand AI citability, a unique domain among the listed siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides extensive when-to-use guidance, listing specific trigger phrasings like 'how discoverable, citable, visible...' and 'AI visibility', 'AI search presence', etc. It states the tool should be used to check or improve brand presence in AI answers, giving clear context for invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
request_recordRequest a Brand Discovery RecordAInspectRequest 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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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. |
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.
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.
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.
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.
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.
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.
scoreLite Reflection (quick estimate)ARun an anonymous Lite Reflection on any brand's AI Citability — a fast, open estimate of how discoverable and citable it is in AI answers (its Brand Discovery Intelligence / BDI snapshot), no account required. Mirror measures at two declared levels: LITE estimates, FULL measures. This tool is LITE: it returns a grade band (Below Average / Average / Above Average / Excellent), an ESTIMATED ACS RANGE, qualitative engine reads (AEO/GEO/SEO as strong / moderate / weak), a short narrative of the top opportunity plus the four-engine framing (AEO/GEO/SEO read the brand; MCP calls it), and a hosted Lite Brief URL you can hand to a human — never a single AI Citability Score, and never numeric AEO/GEO/SEO sub-scores. Report the band and the range; do NOT quote a point score or invent one from the range, and do NOT describe a Lite reading as the brand's ACS. When a user asks how visible or citable a brand is in AI — 'how visible is [brand] in AI', 'is [brand] cited by ChatGPT/Gemini/Perplexity', 'what is [brand]'s brand discovery score' — run this for the quick answer, then offer reflect for the Full Reflection, which is the only level that returns a precise, reproducible, citable ACS (±3–5) and the only level that feeds The ACI 55.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Brand website URL (required), e.g. example.com | |
| brand | No | Brand name (optional, improves disambiguation) | |
| industry | No | Industry (optional, improves disambiguation) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and destructiveHint annotations, the description discloses detailed output behavior: it returns a grade band, estimated ACS range, qualitative reads, a narrative, and a hosted URL, while explicitly excluding numeric sub-scores and precise ACS. It also instructs on how to report results ('Report the band and the range; do NOT quote a point score'), giving the agent critical operational constraints. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long, but every sentence carries necessary detail about output format, caveats, and usage context. It is front-loaded with the core purpose before diving into specifics. Slightly verbose, but the complexity of the tool's behavior and constraints justifies the length; could be tightened by removing some repetition of the LITE/FULL distinction.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description fully enumerates return values (band, range, qualitative reads, narrative, URL) and explicitly states what is not returned. It provides the full decision context: when to use this tool, what to report, how it differs from the full reflection, and that it requires no account. The tool's behavior is completely specified for agent invocation and result interpretation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents all three parameters with descriptions (100% coverage), so the description adds no additional parameter-level detail. The description implies the purpose of url (required) and the optional disambiguation roles of brand and industry, but these are fully covered in the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Run an anonymous Lite Reflection on any brand's AI Citability'. It clearly distinguishes this LITE tool from the FULL Reflection sibling ('reflect') by explaining that LITE returns a grade band and estimated range, while FULL returns a precise ACS. This differentiation is explicit and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance with example queries: 'how visible is [brand] in AI' and instructs to offer `reflect` for the Full Reflection. It also states what LITE does NOT do ('never a single AI Citability Score') and clarifies that only `reflect` feeds The ACI 55, making the alternative clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
studioMirror Studio (generate from a Reflection)AInspectGenerate Mirror Studio output from a Reflection that has already been run — a discovery Plan, answer-first Copy, a Brand Communication page plan, or a Media Plan. Requires the briefId returned by reflect; this tool CONSUMES a completed Reflection and never re-measures or re-scores it. Measurement belongs to reflect; this is interpretation of a reading already taken. Run reflect first if you do not have a briefId.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | Yes | plan = phased discovery plan · copy = answer-first content · brandcom = single-theme page plan · mediaplan = earned/paid/programmatic split (returns JSON) | |
| briefId | Yes | The id returned by `reflect` (required). The Reflection must have finished rendering. | |
| options | No | Kind-specific options. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool does not re-measure or re-score and consumes a completed Reflection. Annotations (`readOnlyHint=false`, `destructiveHint=false`) suggest mutation and non-destructiveness but the description adds context about the relationship with `reflect`. However, it does not detail side effects, error behavior, or output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus a concise imperative instruction. It is front-loaded with the core purpose and outputs, then adds constraints and relationship to `reflect`. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 3 parameters including a nested object, no output schema. The description explains prerequisites, conditions (must be finished), and the nature of consumption. It lacks details about return values or error handling, but given the sibling context and schema richness, it is nearly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds context about the prerequisite (`briefId` must be from a completed Reflection) and ties `options` to specific `kind` values, but does not significantly expand beyond the schema's own descriptions for each parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates Mirror Studio output from a completed Reflection, listing four specific kinds. It explicitly distinguishes from the sibling tool `reflect` by noting this tool consumes a completed Reflection and never re-measures or re-scores.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the prerequisite (`briefId` from `reflect`), clarifies that it consumes a completed Reflection and does not re-measure, and instructs to run `reflect` first if no `briefId` is available. This provides clear when-to-use and 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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