How to integrate Peec AI MCP with Atomic Agent

Archive up to 50 prompts (sets is_archived = true) so they stop running while keeping their chats and history intact.

Assign a built-in classification (Corporate, Competitor, Editorial, Institutional, Other, Reference, UGC, You, Related) or the name of a custom domain classification to a domain.

Assign a built-in classification (Homepage, Category Page, Product Page, Listicle, Comparison, Profile, Alternative, Discussion, How-To Guide, Article, Other) or the name of a custom URL classification to a URL.

Create up to 50 brands (competitors or own) in a project in one call.

Create product categories.

Define a new custom domain classification on a project.

Create a brand and return its global_brand_id.

Create catalog products (source=CATALOG) in a project — the brand's own claimed/uploaded products, as opposed to LLM products that Peec auto-detects in shopping answers.

Create up to 50 prompts in a project in one call.

Create up to 50 tags in a project in one call.

Create up to 50 topics in a project in one call.

Define a new custom URL classification on a project.

Delete up to 50 brands in a project.

Delete categories by id (use list_categories to resolve ids).

Permanently delete a custom domain classification entity.

Delete products by id (use list_products to resolve ids).

Delete up to 50 prompts in a project.

Delete up to 50 user-defined tag groups.

Delete up to 50 tags in a project.

Delete up to 50 topics in a project.

Permanently delete a custom URL classification entity.

Rename, delete and reassign brand-perception attribute clusters — the same edits the Manage attributes screen makes.

Resolve prompt suggestions listed by list_prompt_suggestions, in one pass over a reviewed set: ids in accepted_ids become tracked prompts and promote their topics into the project, ids in rejected_ids are deleted from the review list.

Generate prompt suggestions for a project — the same generation Prompt discovery runs in the app.

Compose candidate topics for the project from its brand profile — the same composition Prompt discovery runs in the app.

Get Peec's opportunity-scored action recommendations for improving brand visibility in AI search engines.

Aggregate AI agent visit counts from access logs over a date range.

How the project's own brand ranks against competitors for each brand-perception attribute.

The sources feeding one brand-perception attribute: the URLs AI answers cited when describing the brand with this attribute.

How AI models describe the project's own brand: attribute clusters (e.

The full competitive breakdown matrix: for every brand-perception attribute, the average prominence of each brand (the project's own brand and its competitors) across AI answers.

The recurring arguments AI models make against the project's brand when a buyer is deciding.

The sources AI models retrieved when asked about one objection.

Get a report on brand visibility, sentiment, and position across AI search engines.

Get the full content of a single chat (one AI engine's response to one prompt on one date).

Aggregate counts and rich-answer-feature shares over a project's chats for a date range.

Get a report on source domain visibility and citations across AI search engines.

Get one product's detailed metrics over a date range — the drill-down companion to list_products, which returns a thin row per product.

Read a project's brand profile — the description, industry, brand-identity adjectives, target markets, audience distribution, and product/service list that Peec uses to generate prompt suggestions.

Read the authoritative status of a run returned by generate_prompt_suggestions.

Compare the qualitative characteristics and features that AI engines associate with a product (or the whole catalog) against competitors.

Return catalog-wide shopping metrics over a date range — avg_visibility, avg_win_rate, and avg_position, each with a period-over-period delta — as a single aggregated row across the filtered scope.

Return a per-bucket shopping time series (visibility, win_rate, avg_position, sov) for specific products or global brands over a date range.

Fetches and returns the content of a URL for downstream analysis — the markdown Peec extracted from the page.

Get a report on source URL visibility and citations across AI search engines.

List all known AI crawler/agent bots that visit the site (e.

The editable attribute taxonomy behind brand perception: every cluster with its id, label and the raw extracted attributes grouped under it.

List brands tracked in a project — includes the user's own brand and competitors.

List the social channels configured for a project's own brand and competitors.

List a project's product categories.

List chats (individual AI responses) for a project over a date range.

List the custom domain classifications defined for a project.

Search Peec's global brand catalog — the shared registry of real-world brands (e.

List the merchants (sellers, e.

List the AI engine channels tracked by Peec.

Deprecated — prefer list_model_channels, which returns stable channel IDs that survive model upgrades.

List a project's products with headline metrics over a date range, filtered by the shopping filter pack.

List active projects the authenticated user has access to.

List prompts (conversational questions tracked daily across AI engines) in a project.

List a project's prompt suggestions — prompts generated by Prompt discovery and awaiting review, which are not tracked and produce no data until accepted.

Every AI assistant Peec can attribute referral traffic to, whether or not a project has seen traffic from it.

List the general web-search queries an AI engine fanned out to while answering prompts in a project over a date range — the text sub-queries it sent to a search index, NOT product/shopping lookups (use list_shopping_queries for those).

Rank the query terms that drive shopping demand by how many distinct chats they appear in, with a trend vs a comparison window.

List products or categories (via entity_type) ranked by shopping performance over a date range.

List the product/shopping queries an AI engine fanned out to while answering prompts in a project over a date range.

List the user-defined tag groups in a project, each with its shared color and tag count.

List tags in a project.

List topics in a project.

List the custom URL classifications defined for a project.

Read the full markdown content of a single Peec documentation page.

Search the Peec docs (https://docs.

Replace the complete social-channel list for up to 50 brands.

Set the project's own brand-identity profile — its industry, target audience, tone, and similar descriptive fields.

Unarchive up to 50 prompts (sets is_archived = false), reactivating them so they resume running.

Clear the classification override on a whole domain (operates on a domain, e.

Clear the classification override on a single URL (operates on one full URL — for a whole domain use unassign_domain_classification), restoring the heuristic classification.

Update up to 50 brands in one call (name, regex, aliases, domains, color per item).

Rename and/or move categories by id (use list_categories to resolve ids).

Update up to 1000 products by id in one call (use list_products to resolve ids).

Update the topic, tags, location and/or fact-checking selection of up to 50 prompts in one call.

Edit up to 50 pending prompt suggestions from list_prompt_suggestions before finalizing: rewrite the text and/or replace the tag set per item.

Rename and/or recolor up to 50 user-defined tag groups in one call.

Update up to 50 tags in one call (name, color, and/or group per item).

Rename up to 50 topics in one call.