Track how AI describes your brand. Save sentiment checks once. Review their daily or weekly history.

Each saved check runs one query and brand through the sentiment primitive. Cite42 stores the positive, neutral, and negative scores, the phrases driving them, and the per-model breakdown for review in the dashboard or through MCP.

  • Daily or weekly
  • Stored run history
  • No subscription
  • MCP accessible
Create trackerInstall MCP

Step 1: Create your account

Get $1 in test credit to try your first calls.

Create free account

Step 2: Connect Cite42 MCP

Replace cite42_live_... with your key, then run this once.

Step 3: Ask inClaude Code

You ask your AIHow do AI answers describe Linear when buyers ask for project management tools for product teams?
MCP runs/v1/sentiment
Runs across the sources shown.
Your AI saysSentiment is mostly positive: fast, opinionated, and developer-friendly. Caveats cluster around reporting depth and non-technical adoption...
TRACKER REPORT QUESTIONS

Ask your AI about stored tracker data.

After a scheduled or manual run, Cite42 stores the structured result and comparable change so your connected AI can retrieve and summarize it through MCP.

  • How does AI talk about my brand?

    You ask your AI“How does AI talk about my brand?”
    MCP runs/v1/sentiment
    Your AI saysThe latest run is 0.62 positive, 0.30 neutral, and 0.08 negative. Its aggregate phrases emphasize setup speed. It includes the key result, source context, and next step...

  • Which model is most negative about us?

    You ask your AI“Which model is most negative about us?”
    MCP runs/v1/sentiment
    Your AI saysClaude has the highest negative score in this run, while ChatGPT has the highest positive score. It includes the key result, source context, and next step...
SENTIMENT HISTORY

Track how AI describes your brand.

Save a brand question, run the sentiment primitive daily or weekly, and keep the per-model scores and phrases that explain the tone.

  • Store positive, neutral, and negative scores per model.
  • Keep the short phrases driving each classification.
  • Review comparable aggregate score deltas over time.
WHAT WE QUERY

Sources Cite42 checks.

Cite42 queries AI answers, search demand, and conversation signals, then returns normalized results your app or agent can use.

  • ChatGPT

    OpenAI · Search when needed

  • Claude

    Anthropic · Search when needed

  • Perplexity

    Perplexity Sonar · Web-grounded

  • Gemini

    Google · Google Search grounding

  • Google AI Overviews

    Google Search · Live SERP

  • Google data

    Google data

    Search demand signal

  • Reddit

    Conversation signal

  • YouTube

    Video demand signal

HOW IT WORKS

Save. Schedule. Review.

Save up to 25 checks, choose daily or weekly, then review every stored report in Cite42 or through MCP.

  1. Choose one brand and one query per check.
    Add more checks to cover several brand-intent questions or different model selections, up to 25 saved checks in one tracker.
  2. Run every saved check daily or weekly.
    Choose the local time and timezone, then confirm the maximum cost permitted for one complete tracker run before activation.
  3. Compare stored scores and phrases.
    Review aggregate and perModel results in Cite42, or retrieve the tracker report through MCP so your AI agent can summarize how the measured tone changed.
EXAMPLE USE CASES

Three ways to use this tracker.

A few practical ways teams use AI sentiment tracker.

  • Saved baseline

    Keep the brand question stable.

    Save the exact question, brand, and selected models so daily or weekly reports remain comparable.
  • Tone history

    Inspect score and phrase changes.

    Review changes in positive, neutral, and negative scores alongside the phrases stored for each model.
  • Agent report

    Ask your AI for the stored results.

    Use MCP to retrieve the tracker history and let your connected AI summarize the structured sentiment data.
PRICING

Runs use normal per-call pricing.

Creating, managing, and reading a tracker is free. Scheduled and manual runs bill the saved /v1/sentiment checks at the listed per-model rate, up to the cost ceiling you confirm when activating.

Underlying primitivePriceWhat you get
POST/v1/sentiment$0.03-$0.09/modelPositive / neutral / negative per model with phrases, $0.03-$0.09/model depending on surface, all five for $0.32
See all primitive prices and source coverage
KEEP EXPLORING

All endpoints

Same API key, same credit balance, different question.

QUESTIONS

Questions about AI sentiment tracker.

It stores every scheduled /v1/sentiment result: the query, brand, perModel scores and phrases, and the aggregate scores and phrases.
No. The sentiment result contains structured scores and short phrases. It does not expose the underlying prose answers as part of this tracker primitive.
No. The sentiment tracker stores the sentiment primitive output, not citation evidence. Use a separate citations tracker when you need cited-source history.
Yes. An authorized AI agent can list trackers and retrieve their stored reports through the tracker MCP tools.
Each saved check uses the normal /v1/sentiment price for its selected models. Tracker management and report access do not add a subscription fee.
The tracker pauses when its current estimated cost exceeds the maximum per-run cost you confirmed. You can review the estimate before reactivating it.
READY WHEN YOU ARE

Create your AI sentiment tracker.

Save brand sentiment checks, choose a daily or weekly cadence, and keep scores and phrase history available in Cite42 and through MCP.

Create trackerInstall MCP