AthenaHQ

When grounding is present, use its complete team-confirmed statement and all supporting facts as joint evidence; the legacy fact ID/text are only a compatibility anchor. List Oracle accuracy findings for a website: places where an AI response contradicted a verified brand fact (type 'inaccuracy' or 'kb_issue', with quote, claim, and reason) or where two knowledge-base facts conflict ('kb_duplicate' / 'kb_contradiction', pairing fact_text with related_fact_text). Findings from every status are included by default (pending, acknowledged, ignored, ...), each row carrying its status — pass the status filter for pending items only. Filters (run_id, fact_id, lineage_id, response_id, prompt_id, source_url, type, kind, severity, status) AND-compose; source_url matches evidence URLs exactly after normalization (protocol and trailing slash ignored). 'total' is the pre-limit count. 'latest_run' is the newest scan of any kind and is null only when the website has never been scanned, so latest_run plus empty findings means it scanned clean, while latest_run null means never scanned, not clean. Archived findings from before the current scanner ARE included: their run carries 'is_imported': true and keeps its original started_at, so an import can be the newest run — say the history is archived rather than calling it a current scan. 'has_imported_history' is true when any such archived run exists, and every finding row carries 'run_is_imported' so archival and current rows stay distinguishable in one list. Returns status 'no_access' if the brand does not have Oracle v2; that response carries the status alone, with no counts or findings.

List personas configured for a website with per-persona prompt counts. Use it to resolve persona_id values returned by other tools to persona names and descriptions.

Fetch a pillar's synthesized markdown document from the Knowledge Base. Returns document: null when the pillar exists but has no synthesized document yet.

List a website's Knowledge Base pillars (excluding archived ones) with approved-fact counts, whether a synthesized document exists, and when each pillar was last researched.

Get a pitch report by ID including competitors, prompts, attributes, top citing sources, and aggregate metrics (brand mentions, sentiment, response rate).

Get cumulative position — average ranking position for each competitor across the date range.

Get position distribution: share of responses where the brand ranks top/middle/bottom across the date range.

Get ranking position over time — average position of the website in AI model responses, grouped by day.

Read Discover history and progress for a website, or supply the run ID returned by discovery launch to inspect one study. Distinguishes pending, running, completed, partial, failed and cancelled. limit bounds returned runs (default 10, maximum 20); has_more means older runs remain. This is read-only: it does not start research, monitor in the background or send notifications. Candidate results can be reviewed in the application's Discover page. Requires permission to view prompts; source visibility restrictions still apply.

List the Default, custom and archived streaming schedules for a website. Use each row's selection value when filtering analytics or responses. Default includes all pre-cutover history and ad hoc runs; archived schedules retain results. To combine schedules, pass several selection values as an array in the schedule field of query_metrics and query_rows, or use a saved view that stores several schedules. Partner REST filters take one schedule. starts_on and ends_on bound a custom schedule's active period as UTC days (null means no bound); an active schedule outside its period does not run.

List prompt tags for a website with per-tag prompt counts. Tag IDs can be used with the prompt_tags filter on the prompts endpoint.

List prompts (search queries) tracked for a website

Fetch a single LLM response by id with full sources, mentions, and rank details.

Read the latest (or a specific) response-streaming run's whole-run state and response-queue progress. Only completed means downstream analysis has finished

Get LLM responses for a website. Shows how AI models answer queries, with sources, sentiment, and ranking data. Filter by whether a model used search queries or by text within those queries. For a variation-level export at volume, pair filters.variation_filter with include_response: false and compact_sources: true. That keeps the prompt, its variation, the model, the mentioned/cited flags and the cited domains while dropping the answer text, cutting each row to roughly a tenth of its size.

List saved views (filter presets) for a website

Read saved Google organic rankings by explicit market and device alongside observed AI visibility. Includes recent history, missing-data reasons, freshness and committed provider budget. Does not trigger collection.

Get cumulative share of voice — overall SOV for each competitor across the date range. Each entry also includes relative_mention_rate: the share of responses mentioning any tracked brand that mention this entity.

Get share of voice over time — how often the website is mentioned vs competitors, grouped by day. Each entry also includes relative_mention_rate: the share of that day's responses mentioning any tracked brand that mention this entity.

Get the Shopping Insights dashboard for a website over a date range: how often the brand's products appear in AI shopping answers (product carousels) versus competitors, average position and price, the top own and competitor products, the competitor leaderboard, retailer (merchant) breakdown, rank distribution, price comparison, and a daily appearances trend. Rates are percentages 0-100; positions are 1-based (lower is better). Requires a paid plan.

List individual cited URLs (pages) for a website with citation, mention, and impression metrics plus a per-URL daily sparkline. Each row is classified as owned / competitor / partner / third_party. Companion to get_sources (root-domain-level). Supports filters (date range, models, prompts, competitors, locations, personas), search, sort, and pagination.

List top cited sources (root domains) for a website with citation, mention, brand-mention, and impression metrics. Each row is classified as owned / competitor / partner / third_party. Supports filters (date range, models, prompts, competitors, locations, personas), sort, and pagination via page_num / page_size.

List topics (groupings of prompts) for a website, with the count of prompts in each

List tracked content (1st-party drafts, imported pages, 3rd-party placements) with citation, mention, and impression metrics for the supplied date range and filters. Supports pagination via page_num / page_size and optional filtering by Content Hub sheet_id or content_type. In-flight pipeline items (drafts, briefs, running generations) never appear here; enumerate those with list_content. A returned item can still lack a url (pipeline finished but not published anywhere yet).

Look up a user by email address, scoped to the caller's organization. Returns 404 when the email does not belong to a member of the organization or of one of its websites.

Enumerate every content item in a website's Content Hub, published or not: in-flight drafts, briefs, snipes, optimize and slice runs, scheduled and failed items, plus tracked pages. Returns identity fields only (id, title, type, stage, sheet, URL, timestamps, target prompt ids), no metrics. Uncached and Postgres-backed, so an item created by create_content is listable immediately. Filter by sheet_id, content_type, stage, or prompt_id. stage is the raw pipeline workflow state, not a publication flag: null = no pipeline record (most tracked external / imported pages), and manual editor items carry 'generated'. Treat an item as published only when its stage is done (or it is an external / imported page) AND it has a url; done without a url is an unpublished orphan. A sheet_id of a view-type tab (see get_content_hub_sheets sheet_type) lists the shared main pool without the view's saved filters. Use get_tracked_content for citation and impression metrics on published content; use this tool to discover what exists. Each row carries prompt_ids: the prompts that item was WRITTEN FOR (what create_content was given), capped at 25 with the true size in prompt_count — resolve the ids to text with get_prompts rather than calling get_content_detail per row, and drill in with get_content_detail where prompt_count exceeds the ids returned (that read carries up to 100; associations past 100 are not currently readable). prompt_ids includes soft-deleted prompts, which get_prompts does NOT list — an id that fails to resolve there is a deleted prompt, and get_content_detail returns its text and status: "deleted". Empty prompt_ids means no targeting was recorded, which is normal for imported or externally tracked pages. This is not the same as being cited for a prompt — see get_content_citation_prompts for that. Filter by prompt_id for the reverse question: which content already targets a given prompt (coverage gaps).

List pitch workspace reports for the organization. Returns all non-deleted pitches with their status and metadata.

Read custom source tags, URL patterns, and revision tokens. These are not prompt tags or built-in categories (Owned, Competitor, Partner, Third party). Read before creating, editing, or deleting. Filter by name substring or tag ID. Use the tag offset to page tags; use a tag ID and pattern offset to page its patterns. Research cited sources with existing source analytics tools before recommending changes. If there are no tags or sources, ask which domains the user wants to organize.

List every website accessible to the calling session. Works with any API key (global or scoped) and with MCP/OAuth sessions. Call with an empty argument object `{}`. Each item includes `baseCountry`, the country market the website targets: use it to tell same-brand websites for different countries apart.

Merge 1-20 source pillars into a published target pillar: every fact on the sources (any review status) re-files onto the target, page links carry over keeping the stronger signal, and the source pillars are then deleted. The target must be a published pillar and must not appear among the sources. Unlike the other Knowledge Base writes, retries are strict: missing source ids are rejected rather than skipped, so re-read the pillars before retrying a failed merge.

File 1-50 brand facts under a published Knowledge Base pillar — works for unfiled facts and re-files facts from other pillars. Review status never changes, and only approved facts under a published pillar feed content generation, so moving is what puts approved facts to work. Missing ids are reported in not_found_ids rather than failing the call, so retries are idempotent.

Move one or more content items to a different Content Hub sheet, or back to the shared pool. Sheet assignment is organizational metadata only: a move never changes an item's stage, brief, body, versions, URL, or metrics, and the item stays fully readable and revisable. targetSheetId must be a "sheet"-type tab from the Content Hub sheets list — view-type tabs (e.g. All content) are computed filters, not assignable; pass null to unassign back to the shared pool they display. Batch is all-or-nothing: if any contentIds entry does not belong to the website, nothing moves. Custom-column values follow each item when the target has a matching column (same name and type; missing columns are auto-created); values whose column conflicts by type are dropped, and multi-select selections whose option is missing on the matched target column are removed — both counts are reported. Each moved item is returned with its previous and new sheet id so no follow-up read is needed.

Open the AthenaHQ AI crawler access panel. Enter a public HTTPS domain to inspect crawler responses and robots.txt permissions without an AthenaHQ account.

Record an already-published article's URL in AthenaHQ for citation and mention tracking. Does not upload or publish content to a website or CMS. Call only after the article is live. Sets the first publication time and moves the pipeline stage to done. A first publish requires a finished article (stage generated), with one exception: an optimize draft is created from a page that is already live and can be marked published while it waits at stage pending, as long as no Athena review is running on it. Briefs, still-generating or failed pieces, optimize drafts with a review in flight, and items with a scheduled CMS publish are rejected with the reason. Pass the exact public URL of the live page (a bare domain path like acme.com/blog/post is fine; the protocol is stripped on store). Calling it again on an already-published item (including external and imported pages, which are live by definition) updates the tracked URL only, keeping the original publish time. This is the only way to set a content URL over MCP; rename_content is deliberately title-only.

Query aggregated metrics for brand performance, competitors, sources, citations, and response trends. Use query_rows for individual records. Mentions: from "response_mentions". Citations: from "citations". On either, where { is_website: true } selects the brand; { is_competitor: true } selects competitors; { entity_id: { eq: "<uuid>" } } selects one entity. Filter/group models by model_category (display names like "ChatGPT", "Claude"), never "model". Dimensions vary by cube; source_mentions has no prompt/model dimensions. Measures: count, count_distinct, avg, sum, min, max; per-measure where enables conditional aggregation. Use compare for two date ranges. When grouping by IDs, use alsoSelect for names: entity_id/entity_name, prompt_id/prompt_text, prompt_topic_id/topic_name (prompts uses topic_id). Cited pages mentioning competitors but not the brand: from "sources", where { source_mentions_entity: { hasSome: [competitorIds], hasNone: [brandId] } }. Also works on "source_mentions". Checks all stored page mentions per URL/website. Date/model on sources scope AI responses; extracted page content may be older than the live page. Content-hub attribution: on "sources", where { is_attributed_content: true }; by: ["content_id"], alsoSelect: ["content_title"]. Add content_type to by for type, or group by content_type alone. Read articles from "contents" via query_rows. Schedule: select one "default" or custom/archived UUID from the catalog, or an array of them to combine schedules. Omit for caller default (current page in Ask Athena); explicit schedule overrides it. Top-level prompt_schedule_kind/id equality filters remain supported but must not conflict with schedule. Historical provenance is independent of current prompt membership; website-level cubes are unaffected.

Query individual analytics rows. Use for qualitative data like response text, specific identifiers, or individual mentions. Schedule selection: pass schedule: "default" or a custom/archived UUID from the schedule catalog, or an array of them to combine schedules. Omit to use the caller default (Ask Athena uses the current page selection). Explicit selection overrides that default. Existing top-level equality predicates on prompt_schedule_kind/id remain supported, but cannot conflict with schedule. Historical provenance does not change with current prompt membership. Website-level cubes are unaffected. Cubes (same set as query_metrics): attributes, citations, competitors, contents, prompts, response_mentions, responses, source_mentions, sources, topics, websites. Returns { rows, nextCursor, hasMore }. Pass nextCursor back as "after" to paginate. On the responses cube, content is hydrated from Postgres and can only be selected. Do not filter or sort by content. Use search_terms_in_responses for text frequency analysis. Use query_metrics for aggregates instead.

Rename a content row's title. URL editing is not supported — URL is the content asset's stable identity and cannot be changed here.

Bring an earlier version's text back as the current draft — use it when a later pass came out worse than an earlier one. Nothing is deleted: the restored text is appended as a new version on top of the history, so the passes in between remain readable and restorable.

Rewrite a content brief from a natural-language instruction (for example: 'add a section on pricing objections', 'cut the competitor comparison'). An AI model performs the rewrite server-side, so the result is generated text, not your literal wording. Records each pass as a version; iterate by calling it again, since each call starts from the current brief. Most useful while a piece waits at status generated_brief, where the brief still shapes what the article will say — call approve_brief once it reads right. It also works after the article exists, to correct the brief for a later regeneration; note that revising the brief does NOT rewrite an already-generated article, so use revise_content for that.

Rewrite an existing content draft from a natural-language instruction (for example: 'tighten the intro', 'add a section on pricing', 'remove the second half'). An AI model performs the rewrite server-side, so the result is generated text, not your literal wording; when you already have the exact replacement text, use edit_content instead. Applies the change to the whole article and records it as a new version, so earlier passes stay recoverable via get_content_versions / restore_content_version. Iterate by calling it again — each call starts from the current text, so no version id is needed. Use this instead of create_content to change a draft; it edits in place rather than generating a new piece. The returned body is always the complete replacement article, never just the edited passage; compare body_word_count with previous_body_word_count to confirm nothing unexpected was lost.

Semantic search over a website's approved brand facts in the Knowledge Base. Reaches facts not filed under any pillar. Returns fact text with confidence, source URL and quote, and the published pillar it belongs to (null when unfiled).

Replace all tag assignments on an existing prompt. For additions or removals, read the prompt's current tags with get_prompts, resolve tag IDs with get_prompt_tags, and pass the full resulting set. Existing assignments omitted from tag_ids are removed; an empty array clears all assignments. Tag definitions and the prompt itself are preserved.

Pause or unpause multiple prompts in one transaction

Start a Discover study to find evidence-grounded, untracked prompt opportunities for a website. Invoke after explicit user intent to start research and review the sources, country, language, requested prompt count and custom instructions with the user. This launches asynchronous external research and model calls; the prompt count is a target, not a guaranteed yield. Sources: website_analysis (website), social (market discussions), search_console (connected Search Console), keyword_gaps (plan-gated keyword opportunities), brand_guidelines (requires custom instructions). Returns the run ID and version when startup succeeds. Prompts are not automatically added to tracking. Poll the discovery history/progress tool with the returned run ID; do not start another study to check status. Requires prompt-management permission and source entitlements. Candidate results can be reviewed in the application's Discover page.

Run tracked prompts against external AI providers to collect new responses for a website. Sends prompt text to the selected providers, stores their responses in AthenaHQ, and consumes response credits. Starts immediately; use only for a user-requested run. Returns a workflow_id; poll get_response_streaming_status over MCP or GET /api/v1/response-streaming/status over the API for progress.

Register existing pages or third-party URLs as tracked Content Hub items so Athena attributes citations and mentions to them. Creates external content rows from bare URLs with no generation and no credit cost. Tracking starts immediately: the normalized URL becomes the citation/metric join key, so pages cited in the website's monitored AI responses accrue metrics with no publish step (use it for guest posts, partner placements, or any page Athena did not generate). Idempotent per URL: a URL already present on the website (any content type) is not duplicated; the response lists it under skipped with the existing content_id and type. Check that type before chaining destructive calls (a skip can resolve to first-party generated content that shares the URL). Rows are created without page text, which citation tracking does not need; the body can be filled from the app later. Duplicate URLs within one call (including tracking-param variants of the same page) collapse to one row, first occurrence wins. Optional sheet_id files rows under a Content Hub tab; view-type tabs resolve to the main pool (null), echoed per row. Optional per-URL fields set the customer's own Content Hub columns on the created row, addressed by column name as shown on the destination tab (text, number, date, checkbox, link, and multi-select columns; unknown multi-select options are created). An unknown column name or a value that does not fit the column type rejects the whole call and nothing is tracked; skipped URLs keep their existing values. If the user names a destination tab, resolve its id with get_content_hub_sheets before tracking. Omitting sheet_id uses the shared pool, not the user's currently open tab. After the call, report created_count and skipped_count separately, name the returned destination_name, and share content_hub_url so the user can open each item. Skipped rows keep their existing tab: use their returned destination and never describe them as newly added or moved.

Update one brand fact's text, source URL, or confidence in the Knowledge Base. Only provided fields change; pass source_url: "" to clear the source. Changing the text creates a new fact (new id) and archives the current one; the new fact keeps the current review status and the response carries it. Source URL and confidence edits update the fact in place. Duplicates across other facts are not rejected here (Knowledge Base dedup owns those). Does not change review status.

Add or remove identifiers on the website's brand, or on one competitor, under the same rules as the Competitors page. Call get_identifiers first. The name and domain cannot be removed. An identifier that is already listed, or that another tracked brand or competitor on this website already uses, is refused, and nothing is saved. New AI responses use the change right away. Past responses are reanalyzed only when someone runs the backfill on the Competitors page, which costs credits; this tool never starts it. changes_awaiting_backfill counts the edits waiting for it, not responses.

Update a location for a website

Update a Knowledge Base pillar's name, description, or status. At least one of name, description, or status must be provided; only provided fields change, and description: "" clears the stored description. Renaming fails with a conflict when another pillar already uses the name. Status accepts published or archived: archiving hides the pillar from get_pillars and stops its facts feeding content generation, while keeping the facts stored.