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AI Recommendation Check

by com.uxcontinuum
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Does ChatGPT recommend a local business? Live AI-engine check plus who gets named instead.

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Status
Healthy
Uptime
100.0% over 22 days
Last Tested
Transport
Streamable HTTP · MCP 2025-11-25
URL

TDQS

A4.6/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct roles: check_ai_recommendation starts a check, while get_ai_recommendation_result only reads the result of a prior check by check_id. Descriptions explicitly state 'It does not start a new check,' eliminating any overlap.

Naming Consistency5/5

Both names use consistent snake_case with a verb_noun pattern (check_ai_recommendation, get_ai_recommendation_result). The shared 'ai_recommendation' stem makes their relationship obvious.

Tool Count4/5

Two tools is thin on its own, but the async start-and-poll design genuinely requires both a starter and a result reader, so neither is redundant. It is well-scoped for a single-purpose checker.

Completeness4/5

The start/retrieve lifecycle is fully covered, including statuses and polling guidance, so there are no dead ends. Minor gaps exist (no listing of past checks or explicit cancellation), but the core workflow is complete.

Available Tools

2 tools
check_ai_recommendationCheck if AI recommends a businessA
Idempotent
Inspect

Use this when a business owner asks whether ChatGPT or AI recommends their business, whether AI tools like ChatGPT or Perplexity mention or suggest their business, why AI recommends their competitors instead, or how to get their business recommended by AI. It runs a real check: it asks ChatGPT, Perplexity and Gemini up to 3 questions a customer would ask when looking for this kind of business in the area the business serves, and reports whether each engine named the business, which businesses they named instead, and one fix based on the pages the answers cited. Timing: a check usually finishes in under a minute. This tool waits up to about 20 seconds and returns the result when it is ready; otherwise it returns a check_id with the status queued or running, and get_ai_recommendation_result returns the result for that check_id. A saved result for the same business is returned right away. Business details are sent to Continuum (uxcontinuum.com) to run the check. Results are shown in the conversation. Not covered: general SEO or Google ranking questions, writing marketing copy or ads, looking up or checking a person (it checks businesses only), and general questions about how AI search works. Limits: 3 buyer questions per website per day; saved results are reused for 7 days.

NameRequiredDescriptionDefault
emailNoOptional. The owner's email address, if they want Continuum to send this result and follow up.
websiteYesThe business website, for example "joesplumbing.com". Used to recognise the business in answers and to keep its daily limit.
categoryYesWhat the business is, in the words a customer would use to look for one, for example "plumber", "wedding photographer" or "HVAC company". Not a slogan.
service_areaNoThe area the business serves, as its customers would name it, for example "Austin, TX". This describes the business, not the user's own location. Empty when the business serves customers anywhere online.
business_nameYesThe business name as customers know it, for example "Joe's Plumbing".

TDQS

A4.6/5.0
Behavior5/5

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

Far beyond the annotations, it discloses timing (usually under a minute), the ~20s wait-then-return-check_id behavior, immediate return of cached results, data being sent to a third party (Continuum, uxcontinuum.com), a rate limit of 3 buyer questions per website per day, and a 7-day result reuse window. Nothing here contradicts readOnlyHint=false / idempotentHint=true, since the tool initiates external checks and reuses saved results.

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?

Front-loaded with the triggering scenario, then behavior, then exclusions and limits in clearly labelled segments. It is long, but nearly every clause carries operational value; only a few lines ('Results are shown in the conversation') are close to filler.

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?

Despite no output schema, the description explains what comes back (which engines named the business, which competitors they named, one fix based on cited pages) plus the async contract, timing, privacy and limits. An agent has everything needed to call it and interpret the result.

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 all five parameters thoroughly. The description reinforces how the inputs are used (buyer-style questions, area the business serves, recognition of the website) but adds no syntax or format detail the schema lacks; baseline 3 applies.

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 states a specific action and resource: it runs a live check asking ChatGPT, Perplexity and Gemini whether they recommend a named business. It explicitly distinguishes itself from the sibling by naming get_ai_recommendation_result as the polling tool for a queued/running check_id.

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?

It opens with concrete trigger conditions (owner asks whether AI recommends them, why competitors get recommended, how to get recommended) and closes with an explicit 'Not covered' list (general SEO, marketing copy, checking a person, general AI-search theory). The alternative for async results is named with the exact condition that selects it.

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

get_ai_recommendation_resultGet the AI recommendation check resultA
Read-onlyIdempotent
Inspect

Use this to read the result of a check started with check_ai_recommendation, using the check_id it returned. Returns the status (queued, running, done, failed or refused). When done: for ChatGPT, Perplexity and Gemini, how many of their answers named the business, which businesses they named instead, and one fix. While a check is queued or running, poll_after_s is the suggested wait in seconds before reading it again. It waits up to about 20 seconds for a running check to finish. It does not start a new check.

NameRequiredDescriptionDefault
check_idYesThe check_id returned by check_ai_recommendation.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds substantial context beyond them: the enumerated status values, what data arrives when done, the ~20 second internal wait, and the fact it won't launch a check. This is exactly the extra behavioral detail the structured fields cannot convey.

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?

Five sentences, all front-loaded around the core action first, then return semantics, then polling behavior, then the negative constraint. Every sentence carries distinct information with no padding.

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?

With no output schema, the description carries the return-value burden and does so fully, enumerating statuses and the completion payload (counts, competitor names, one fix). Nothing an agent needs to call or interpret this tool is missing.

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?

Schema coverage is 100% and the single check_id parameter is fully documented there, so baseline is 3. The description adds value by tying the parameter to its producer (the check_id returned by check_ai_recommendation), clarifying origin rather than repeating the 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?

States a specific verb (read) and resource (the result of a check), names the sibling that creates it (check_ai_recommendation), and explicitly disambiguates by saying it does not start a new check. An agent can distinguish it from its sibling immediately.

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?

Gives the precise precondition (use the check_id returned by check_ai_recommendation) and the polling protocol: while queued/running, poll_after_s is the suggested wait. It also rules out the alternative action ('does not start a new check'), leaving nothing to inference.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool update
    • Changedcheck_ai_recommendation2 fields changed
      • changedInput schema / properties / email / description
        Previous value: -"The business owner's email address, for example \"jane@joesplumbing.com\". Sent to Continuum with the business details; Continuum may email the owner personally about the result."New value: +"Optional. The owner's email address, if they want Continuum to send this result and follow up."
      • changedInput schema / required
        Previous value: -[
-  "email",
-  "business_name",
-  "website",
-  "category"
-]New value: +[
+  "business_name",
+  "website",
+  "category"
+]
  2. 1 tool update
    • Changedcheck_ai_recommendation2 fields changed
      • changedInput schema / properties / email / description
        Previous value: -"The business owner's email address, for example \"jane@joesplumbing.com\". Required. Continuum may email the owner personally about the result; nothing is sent automatically."New value: +"The business owner's email address, for example \"jane@joesplumbing.com\". Sent to Continuum with the business details; Continuum may email the owner personally about the result."
      • changedInput schema / properties / service_area / description
        Previous value: -"The area the business serves, as its customers would name it, for example \"Austin, TX\". This describes the business, not the user's own location. Leave empty only if the business serves customers anywhere online."New value: +"The area the business serves, as its customers would name it, for example \"Austin, TX\". This describes the business, not the user's own location. Empty when the business serves customers anywhere online."
  3. 2 tool updates
    • First observedcheck_ai_recommendation
    • First observedget_ai_recommendation_result

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