Conductor

Brand mention analysis for AI search engines (ChatGPT, Perplexity, Gemini, Google AIO, etc.). Reports which brands are MENTIONED in AI-generated answers — market share, sentiment scoring, and competitive positioning across time periods. NOT for website/URL citations (use ai_citation_insights) or Google SERP rankings (use keyword_insights). REQUIRED: account_id is mandatory. Call tracked_configs with config_type=accounts to discover your account_id. DEFAULT MODE returns per-brand market share rankings. Boolean switches activate alternate views: - brand_sentiment_trend: democratic sentiment score over time (each prompt gets one equal vote) - sentiment_by_brand: per-brand positive/negative/neutral counts and score - sentiment_statements: individual quote/statement drilldown (capped at statement_limit, default 1000) - sentiment_by_source: per-source-domain sentiment breakdown - intent_performance / persona_performance: brand coverage % by intent or persona (pair with owned_only=true) - persona_intent_performance: coverage x sentiment heatmap by persona+intent (pair with owned_only + web_property_id) - topic_coverage: brand coverage % per topic (pair with owned_only + web_property_id) - topic_breakdown: market share grouped by topic — report share_of_topic (within-topic), NOT market_share_percent (global) COLUMN BINDING: default mode rows carry both market_share_percent AND brand_sentiment_score. Report market_share_percent for "market share" / "who's winning" questions. Report brand_sentiment_score for "sentiment" questions (or switch to a sentiment mode for deeper analysis). EMPTY RESULTS: if a filter returns no data, validate filter values via tracked_configs (config_type=topics, brands, etc.) — the name may not match configuration exactly. Use EXACT parameter names: search_engine (NOT search_engine_name), topic (NOT topic_name), persona (NOT persona_name), intent (NOT intent_name), language (NOT language_name). Array parameters accept JSON arrays. Omit a filter for "all values" on that axis.

ai_brand_insights

MCP endpoint for AI prompt fan-out (fan-out query) analysis. When an AI engine runs a web search to answer a tracked prompt, it decomposes the prompt into multiple targeted fan-out queries (the "fan-out"). This tool exposes those fan-out queries, flags which fan-out queries are BRANDED — a brand counts only when it appears as a whole word/phrase in the fan-out query text (brand 'Arc' matches 'arc crm pricing', not 'architecture'), measures fan-out query specificity, and reports how fan-out depth shapes brand mentions and citation diversity. Modes include brand_impact (per-brand fan-out query correlation) and provider_comparison (cross-provider fan-out analysis for shared queries). REQUIRED: account_id is mandatory. Call tracked_configs with config_type=accounts to discover your account_id. mode=records (DEFAULT) — one row per engine RESPONSE with fan-out queries, branded split, brands, citations, counts, and avg_fan_out_query_token_count. ALWAYS narrow with filters. mode=fan_out_queries — one row per INDIVIDUAL fan-out query (exploded) with text, position, fan_out_query_is_branded, fan_out_query_token_count. ALWAYS filter. mode=metrics — ONE row of summary stats: avg/min/max/median/p90/p95 fan-out, histogram, branded stats, avg_fan_out_query_specificity. mode=breakdown + dimension=<X> — one row per group ranked by avg fan-out. mode=trend + dimension=<X> — group aggregates per collection date. mode=brand_impact — per-brand fan-out query correlation: how often each brand appears in fan-out queries and how that correlates with citations. mode=provider_comparison — cross-provider fan-out for queries seen in 2+ providers. mode=discover + dimension=<X> — distinct values of dimension X. mode=brand_fan_out_query_share - one row per brand: of ALL individual fan-out queries run in the window, how many mention the brand (whole-word) and what share that is. Zero-share brands are included. Use for "what fraction of decomposed queries are about brand X"; for the response-level question ("in what % of responses mentioning X did it appear in the fan-out") use brand_impact instead. mode=outliers - responses whose fan-out exceeds the p90 of whatever slice your filters select. Each row carries p90_threshold and account_avg_searches so the comparison is self-contained. Use for "unusually deep searching" questions; no threshold needed. COLUMNS PER MODE (plan follow-up calls if a field you need is absent): - records: date, prompt, topic, prompt_type, intent, persona, ai_search_engine, data_provider, amount_of_searches, fan_out_queries, branded_fan_out_queries, unbranded_fan_out_queries, has_branded_fan_out_query, brands, citations, brand_count, citation_count, distinct_searches, distinct_citation_domains, fan_out_bucket, avg_fan_out_query_token_count, web_search - fan_out_queries: date, prompt, topic, prompt_type, intent, persona, ai_search_engine, data_provider, amount_of_searches, fan_out_query, fan_out_query_index, fan_out_query_is_branded, fan_out_query_token_count, fan_out_bucket, brands, brand_count, citation_count, distinct_citation_domains - metrics: fan_out_responses, distinct_prompts, avg/min/max/median/p90/p95_searches, bucket_under_2, bucket_2_4, bucket_5_7, bucket_8_10, bucket_11_plus, responses_with_branded_fan_out_query, avg_branded_fan_out_queries, avg_brand_count, avg_citation_count, avg_distinct_searches, avg_distinct_citation_domains, avg_fan_out_query_specificity - outliers: records columns (minus branded arrays/web_search) + p90_threshold, account_avg_searches - breakdown: dimension_value, prompt_type (only when dimension=prompt), fan_out_responses, distinct_prompts, avg_searches, max_searches, median_searches, responses_with_branded_fan_out_query, avg_branded_fan_out_queries, avg_brand_count, avg_citation_count, avg_distinct_searches, avg_distinct_citation_domains, avg_fan_out_query_specificity - trend: breakdown columns (no prompt_type/median) keyed by date + dimension_value - brand_impact: brand_name, brand_rank, responses_mentioning_brand, distinct_prompts_mentioning_brand, responses_with_brand_in_fan_out_query, fan_out_query_appearance_rate, avg_fan_out_when_mentioned, avg_response_brand_count_when_mentioned, avg_response_citation_count_when_mentioned, avg_response_distinct_domains_when_mentioned - brand_fan_out_query_share: brand_name, brand_rank, fan_out_queries_mentioning_brand, total_fan_out_queries, fan_out_query_share, distinct_prompts_with_brand_in_fan_out_queries - provider_comparison: prompt, data_provider, responses, avg_searches, max_searches, avg_brand_count, avg_citation_count, avg_distinct_domains, responses_with_branded_fan_out_query, avg_fan_out_query_specificity - discover: dimension_value, fan_out_responses

ai_query_fan_out_insights

Discovery endpoint for account tracking configuration. Returns what topics, prompts, prompt groups, search engines, locales, brands, web properties, sentiment categories, personas, and intents are configured for an account. v2 changes: prompts and topics_with_prompts now include collection_frequency (DAILY, WEEKLY, or MONTHLY). CALL THIS FIRST before querying ai_brand_insights, ai_citation_insights, or keyword_insights whenever you need to discover valid filter values — exact topic names, brand names, locale codes, web property IDs, search engine IDs, etc. REQUIRED: account_id and config_type are both mandatory. DEFAULTS: tracked_configs returns both ACTIVE and INACTIVE items by default. The data tools (ai_brand_insights, ai_citation_insights) default to ACTIVE-only via their topic_status and prompt_status filters. CAPPED RESULTS: the default limit is 100, but this is NOT a hard cap for topics, prompts, or accounts — pass a higher limit (e.g. limit=500) to retrieve more. Do NOT conclude a name or value is unconfigured solely because it does not appear in a default-limit browse. Either raise the limit or use a more specific filter to narrow the search. IMPORTANT: Use EXACT parameter names as documented: - config_type (NOT type, NOT entity_type) - topic_status (NOT status) - prompt_status (NOT query_status)

tracked_configs

Google SERP keyword analysis — traditional search rankings, visibility, and search volume. NOT for AI search brand mentions (use ai_brand_insights) or AI search website citations (use ai_citation_insights). REQUIRED: account_id is mandatory. Call tracked_configs with config_type=accounts to discover your account_id. Default analysis_mode is keyword_performance (visibility rank buckets over time). Other modes: result_types, seasonality (monthly MSV), keyword_grid (ranked keyword list with rank changes), keyword_groups, locations, rank_comparisons, and keyword_details deep dives (keyword_details_summary, keyword_details_rank_over_time, keyword_details_seasonality, keyword_details_search_results) for a single tracked keyword phrase. See x-context.choosing_analysis_mode for question-pattern to mode routing (e.g. "which keywords improved most" → keyword_grid). Discovery and IDs — call tracked_configs with account_id and config_type to get IDs: 1. config_type=web_properties for site IDs, search_engines for engine IDs, locales for locale codes. 2. Use returned IDs in web_property_id, web_property_group_ids, rank_source_ids, device_ids, locodes. Do not guess IDs from display names. Filter semantics: omitted optional array filters (rank_source_ids, device_ids, locodes, keyword_group_ids, etc.) mean no restriction on that axis (all values in account scope). Empty web_property_id + empty web_property_group_ids = all client web properties. keyword_details_* modes override this: exactly one engine, device, and location must be supplied. rank_type: TRUE (default) includes AI Overview / featured snippet positions; STANDARD counts only traditional blue-link ranks. AIO_REFERENCE_RESULT in search results indicates the page was referenced by an AI Overview, not a standard organic result. Stacked bar charts from keyword_performance: follow KeywordInsightsResponse visualization notes (segment order and colors matching the Conductor Keywords UI; best ranks at the top of each bar).

keyword_insights

Website citation market share across AI search engines (ChatGPT, Perplexity, Gemini, Google AIO, etc.). Reports which WEBSITES/DOMAINS are being CITED (linked as sources) in AI-generated answers — domain-level share, top cited pages, and per-prompt URL drilldown. NOT for brand mentions (use ai_brand_insights) or Google SERP rankings (use keyword_insights). REQUIRED: account_id is mandatory. Call tracked_configs with config_type=accounts to discover your account_id. DEFAULT MODE returns domain-level citation market share per time period. Boolean switches activate alternate views: - url_drilldown: per-URL breakdown for specific domain(s). REQUIRES citation_domain_label from a prior marketshare call at the SAME granularity — never guess a label. Returns capped top-N URLs; compare count against the marketshare total to assess completeness. - top_cited_pages: top cited pages across all domains (owned_only=true for your pages) - intent_performance / persona_performance: citation coverage % by intent or persona - persona_intent_performance: coverage heatmap by persona+intent - topic_coverage: citation coverage % per topic - prompt_breakdown / topic_breakdown / search_engine_breakdown: add grouping dimensions URL DRILLDOWN WORKFLOW: 1. Call without url_drilldown to get marketshare results with citation_domain_label values 2. Call with url_drilldown=true + citation_domain_label=["exact label from step 1"] Never guess citation_domain_label — it must come from a prior marketshare result. EMPTY RESULTS: validate filter values via tracked_configs (config_type=topics, etc.). Use EXACT parameter names: citation_granularity (NOT group_by), search_engine (NOT search_engine_name), topic (NOT topic_name), persona (NOT persona_name), intent (NOT intent_name), language (NOT language_name), prompt (NOT query). Array parameters accept JSON arrays. Omit a filter for "all values" on that axis.

ai_citation_insights