AI brand sentiment MCP and REST API. Positive, neutral, or negative, per model.

Ask your AI agent how the models talk about your brand, or call /v1/sentiment directly. Cite42 scores positive, neutral, and negative language and pulls out the phrases driving the tone.

  • $1 free on signup
  • Credits never expire
  • No subscription
  • MCP native
Get $1 free, try itInstall 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...
ENDPOINT PROMPTS

Ask for AI sentiment data.

Cite42 calls /v1/sentiment and returns structured data your AI can summarize.

  • How does AI talk about my brand?

    You ask your AI“How does AI talk about my brand?”
    MCP runs/v1/sentiment
    Your AI saysMostly positive: 62% positive, 30% neutral, 8% negative. Positive answers praise the agent UI and 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 is the most cautious model. It still rates you slightly positive, but flags pricing and limited integrations more often. It includes the key result, source context, and next step...
AI SENTIMENT

Score how AI describes your brand.

Use sentiment scoring to turn AI answer text into structured tone, themes, positives, negatives, and source-backed context.

  • Track positive, neutral, and negative language.
  • See the phrases AI repeats about your brand.
  • Connect sentiment back to the cited answer.
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

EXAMPLE USE CASES

Three ways to use this endpoint.

A few practical ways teams use AI sentiment.

  • Tone baseline

    Map the current narrative.

    Check how AI answers describe your brand across common category prompts, including repeated strengths, caveats, and outdated claims.
  • Risk watch

    Catch negative themes.

    Monitor repeated complaints, caveats, or outdated claims before they spread into sales decks, reports, or executive summaries.
  • Report

    Summarize brand perception.

    Give teams a structured sentiment readout with the answer text and cited context behind each score.
PRICING

Pay per call for this endpoint.

Top up once ($25 minimum). Credits never expire, and this endpoint only charges when it runs.

EndpointPriceWhat 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
POST/v1/search$0.01-$0.07/modelFull prose answers per model, the raw text sentiment is scored from
See all endpoints and the source coverage table
KEEP EXPLORING

All endpoints

Same API key, same credit balance, different question.

QUESTIONS

Questions about AI sentiment.

It scores how AI talks about a brand for a prompt: positive, neutral, and negative ratios, plus phrases that explain the tone.
No. It is based on AI answers. Reviews may influence those answers, but the endpoint measures what the selected AI sources say now.
Each source uses different retrieval, ranking, and summarization behavior. One may cite a critical review while another cites a homepage or comparison page.
Yes. The response includes phrases driving positive and negative tone, so you can see whether the issue is pricing, support, integrations, accuracy, or another theme.
Sometimes. If AI does not know the brand yet, the useful signal is absence: which prompts miss you and which competitors set the category tone.
Keep a fixed prompt set, rerun it on a schedule, and watch for changes in negative ratio and repeated negative phrases.
READY WHEN YOU ARE

Give your AI real sentiment data.

Return the phrases, scores, and examples behind how each model talks about a brand.

Get $1 free, try itInstall MCP