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Profound MCP gives AI assistants read-only access to Profound’s Answer Engine Optimization (AEO), brand visibility, citation, sentiment, shopping, and Agent Analytics data. Use this page to understand what the hosted MCP server can do before connecting it to an MCP client. When you’re ready to connect, refer to the Connection guides.

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How the tools work together

Most conversations start with the discovery tools, then move into reports. Here’s what that typically looks like:
1

Confirm access

Ask what data you can see. The assistant confirms the signed-in user, available organizations, regions, and entitlements with whoami.
2

Find the right scope

Ask about the brand or site you care about. The assistant resolves it with list_organizations, then list_categories for visibility reports or list_domains for traffic reports.
3

Add optional filters

Ask to narrow the question to a market, AI engine, prompt set, topic, or tag. The assistant resolves those filters with list_regions, list_models, list_tags, list_topics, and list_prompts.
4

Run a report

Ask about what you want reported. The assistant retrieves visibility, sentiment, citations, prompt answers, shopping, referrals, or bot crawl data for the date range you name.

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Behavior and safety

All tools are read-only. They retrieve analytics data, but don’t create, update, or delete anything in Profound. All report tools set the following MCP hints: Dates are ISO 8601 strings in YYYY-MM-DD format. Reports validate start_date and end_date and return an actionable error if the date window is invalid.

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Visibility reports

These tools are scoped to a category_id and a date range. Use them to understand how brands appear in AI answers, both in regular text answers and in shopping mode results.

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Brand visibility reports

Use these tools to understand how brands appear in AI answers, how those answers feel, and which sources AI engines cite.
Measures how often and how prominently a brand appears in AI answers for a category over a date range.Example prompts:
  • “How visible were we in AI answers last month, broken down by model?”
  • “Are we gaining or losing visibility against competitors this quarter?”
Default metric: visibility_score.Other useful metrics include share_of_voice, mentions_count, executions, and average_position. Useful dimensions include date, region, topic, model, prompt, tag, and persona.Inputs
Measures sentiment in AI answers for a category over a date range.Example prompts:
  • “How do AI answers feel about our pricing?”
  • “Is sentiment about our brand improving month over month?”
Default metrics: positive, negative, and occurrences. positive and negative are weighted aggregates; occurrences is a raw count.Inputs
Shows which sources AI engines cite for a category, and how often, over a date range.Example prompts:
  • “Which sites do AI engines cite most in our category?”
  • “How often do AI answers cite our own domain?”
Default metrics: count and citation_share. Useful dimensions include hostname, path, root_domain, url, model, topic, prompt, tag, and persona.
root_domain_filter must be paired with dimensions: ["root_domain"].
Inputs
Retrieves the answers AI engines gave for a category’s prompts over a date range: the raw text behind the visibility metrics.Example prompts:
  • “What did AI engines answer for our savings prompts last week?”
  • “Pull the answers behind last month’s visibility drop.”
Inputs

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Shopping visibility reports

Shopping analysis data is currently available only for ChatGPT.
Use these tools to understand how brands, products, and retailers appear when AI answers a prompt with shopping mode results.
Start by asking the assistant how often the category’s prompts return shopping results (get_shopping_trigger_rate_report). If they rarely do, the other shopping reports have little data to show.
Measures how often each brand appears when ChatGPT returns shopping results. This is the shopping counterpart to get_visibility_report.Example prompts:
  • “Which brands appear most in ChatGPT shopping results for our category?”
  • “How does our shopping visibility compare to competitors, day by day?”
InputsNotes:
  • Every row carries an asset object of {name, owned}, and asset is always an implicit grouping key: results are one row per asset and group-by bucket.
Measures individual product visibility inside AI shopping results, one row per product plus any group-by bucket.Example prompts:
  • “How visible are our products in ChatGPT shopping results?”
  • “Where is our flagship product sold, and at what price?”
InputsNotes and tips:
  • The default metrics are visibility_score, average_position, visibility_rank, position1_percentage, position2_percentage, position3_percentage, position_above3_percentage, product_rating, and product_num_reviews.
  • The position metrics are how your assistant tells “always shown, always fourth” from “sometimes shown first”. Each one is a raw 0–1 fraction of the product’s appearances at that slot, and together they sum to about 1, so 0.30 is 30%.
  • include_merchants mode accepts no group_by or target_product, and the position-frequency metrics aren’t available in it.
Measures which retailers appear in a category’s shopping results.Example prompt:
  • “Which retailers does ChatGPT surface for our category?”
InputsThe view input decides what each row represents and which metrics are available.Each view accepts its own metrics, and the server rejects anything outside the set:Notes and tips:
  • This report has no merchant or product filter, so you can’t narrow the results to one retailer. Instead, narrow by topic, region, persona, prompt, or tag, then look up the retailer you care about in the returned rows.
  • The info.view metadata field names the view the server applied. view is optional and defaults to distribution, so if the rows don’t look like what you expected, check info.view to see which view produced them.
Measures how often prompts return shopping mode results. This is the denominator behind the other shopping reports, the assistant can use it to explain a thin or empty result.Example prompts:
  • “How often do our prompts trigger shopping mode results?”
  • “Which topics trigger shopping mode most often?”
InputsNotes and tips:
  • The default metrics are total_runs, shopping_triggered_runs, and trigger_rate_percentage.
  • Despite its name, trigger_rate_percentage is a decimal fraction between 0 and 1, so 0.17 means 17%.
  • Group results by prompt or topic to see which prompts or topics trigger shopping results most often. Those are the places where shopping visibility is worth optimizing.

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Traffic reports

These tools are scoped to a tracked domain, not a category. The assistant resolves the domain with list_domains first and passes the exact hostname Profound returns.
Measures visits a domain received from AI engines, such as ChatGPT and Perplexity, over a date range.Default metric: visits. Useful dimensions include referral_type, referral_source, and date.Inputs
Measures AI crawler activity against a domain over a date range, including bots such as GPTBot and PerplexityBot.Example prompts:
  • “Which AI crawlers visit our domain?”
  • “Is GPTBot crawling us more since the site update?”
Default metrics: count and citations. Useful dimensions include bot_provider, bot_name, bot_type, and date.Inputs

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Resources

Profound MCP also exposes read-only MCP resources: static reference material that an MCP client can load into context. The {term} slot accepts any slug from the glossary index, which includes the metrics and report concepts behind this page’s tools: