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Profound MCP gives AI assistants read-only access to Profound’s Answer Engine Optimization (AEO), brand visibility, citation, sentiment, and agent analytics data. Use this page to understand what the hosted MCP server can do before connecting it to an MCP client. To set up the server, refer to the Connection guides.

How the tools work together

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

Confirm access

Use whoami to confirm the signed-in user, available organizations, regions, and entitlements.
2

Find the right scope

Use list_organizations, then list_categories for visibility reports or list_domains for traffic reports.
3

Add optional filters

Use list_regions, list_models, list_tags, list_topics, and list_prompts to narrow a report to the relevant market, AI engine, prompt set, topic, or tag.
4

Run a report

Use the visibility, sentiment, citations, prompt answers, referrals, or bot crawl tools to retrieve analytics for a date range.

Behavior and safety

All tools are read-only. They retrieve analytics and reference data, but don’t create, update, or delete anything in Profound. The following MCP hints are set on all report tools: 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.

Discovery tools

Use discovery tools to resolve human-readable context into IDs that report tools can use.
Confirm the authenticated user, organizations, regions, and entitlements available to this MCP session.Inputs: none.
List the organizations the authenticated user can access. Returned IDs feed category, domain, and report tools.Inputs: none.
List geographic regions configured for an organization. Omit org_id to see regions across all accessible organizations.Example:
List the AI models Profound tracks. Use returned model IDs to filter reports to a single engine.Inputs: none.
List tracked categories, markets, or segments in an organization. Most brand visibility reports are scoped to a category.Example:
List tracked domains for an organization. Domains are exact hostnames, so www.example.com and example.com are distinct.Example:
List tags available within a category for filtering prompts and reports.Example:
List topics available within a category for filtering prompts and reports.Example:
List prompts configured in a category. Prompts are the questions Profound runs against AI engines to measure brand visibility.Example:

Brand visibility reports

These tools are scoped to a category_id and a date range. Use them to understand how brands appear in AI answers, how those answers feel, and which sources AI engines cite.
Measure how often and how prominently a brand appears in AI answers for a category over a date range.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.Example:
Measure sentiment in AI answers for a category over a date range.Default metrics: positive, negative, and occurrences. positive and negative are weighted aggregates; occurrences is a raw count.Example:
See which sources AI engines cite for a category, and how often, over a date range.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"].
Example:
Retrieve the actual answers AI engines gave for a category’s prompts over a date range.Example:

Traffic reports

These tools are scoped to a tracked domain, not a category. Use list_domains first and pass the exact hostname returned by Profound.
Measure 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.Example:
Measure AI crawler activity against a domain over a date range, including bots such as GPTBot and PerplexityBot.Default metrics: count and citations. Useful dimensions include bot_provider, bot_name, bot_type, and date.Example:

Resources

Profound MCP also exposes read-only MCP resources: static reference material that an MCP client can load into context.
The glossary defines recurring Profound terms such as visibility score, share of voice, citations, prompts, and AI engines.A client typically loads file:///profound/glossary once, then fetches a specific term with file:///profound/glossary/{term} when it needs a precise definition. For example, use file:///profound/glossary/visibility-score for the visibility score definition.
The glossary includes 38 terms across seven categories: Identity, Taxonomy, AI Engines, Prompts, Reports, Metrics, and Agents.Each term has a stable lowercase hyphenated slug, short summary, full description, examples, and see-also references to related terms.