Peec AI
get_brand_report
Get a report on brand visibility, sentiment, and position across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount, total}. Each row is an array of values matching column order. Columns: - brand_id — the brand ID - brand_name — the brand name - visibility: 0–1 ratio — fraction of AI responses that mention this brand. 0.45 means 45% of conversations. - mention_count: number of times the brand was mentioned - share_of_voice: 0–1 ratio — brand's fraction of total mentions across all tracked brands - sentiment: 0–100 scale — how positively AI platforms describe the brand (most brands score 65–85) - position: average ranking when the brand appears (lower is better, 1 = mentioned first) - Raw aggregation fields (for custom calculations): visibility_count, visibility_total, sentiment_sum, sentiment_count, position_sum, position_count When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4) - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id, tag_id, topic_id, prompt_id, brand_id, country_code, chat_id.
Other tools also called get_brand_report?
See providers with this name
Input Schema
{
"type": "object",
"properties": {
"limit": {
"type": "number",
"default": 100,
"maximum": 10000,
"minimum": 1,
"description": "Maximum number of results to return"
},
"offset": {
"type": "number",
"default": 0,
"minimum": 0,
"description": "Number of results to skip"
},
"filters": {
"type": "array",
"items": {
"anyOf": [
{
"anyOf": [
{
"type": "object",
"required": [
"field",
"operator",
"values"
],
"properties": {
"field": {
"type": "string",
"const": "model_id"
},
"values": {
"type": "array",
"items": {
"enum": [
"chatgpt-scraper",
"gpt-4o",
"gpt-4o-search",
"gpt-3.5-turbo",
"llama-sonar",
"perplexity-scraper",
"sonar",
"gemini-2.5-flash",
"gemini-scraper",
"google-ai-overview-scraper",
"google-ai-mode-scraper",
"llama-3.3-70b-instruct",
"deepseek-r1",
"claude-3.5-haiku",
"claude-haiku-4.5",
"claude-sonnet-4",
"grok-scraper",
"microsoft-copilot-scraper",
"grok-4"
],
"type": "string"
},
"minItems": 1
},
"operator": {
"enum": [
"in",
"not_in"
],
"type": "string"
}
}
},
{
"type": "object",
"required": [
"field",
"operator",
"values"
],
"properties": {
"field": {
"type": "string",
"const": "tag_id"
},
"values": {
"type": "array",
"items": {
"type": "string"
},
"minItems": 1
},
"operator": {
"enum": [
"in",
"not_in"
],
"type": "string"
}
}
},
{
"type": "object",
"required": [
"field",
"operator",
"values"
],
"properties": {
"field": {
"type": "string",
"const": "topic_id"
},
"values": {
"type": "array",
"items": {
"type": "string"
},
"minItems": 1
},
"operator": {
"enum": [
"in",
"not_in"
],
"type": "string"
}
}
},
{
"type": "object",
"required": [
"field",
"operator",
"values"
],
"properties": {
"field": {
"type": "string",
"const": "prompt_id"
},
"values": {
"type": "array",
"items": {
"type": "string"
},
"minItems": 1
},
"operator": {
"enum": [
"in",
"not_in"
],
"type": "string"
}
}
},
{
"type": "object",
"required": [
"field",
"operator",
"values"
],
"properties": {
"field": {
"type": "string",
"const": "country_code"
},
"values": {
"type": "array",
"items": {
"enum": [
"AE",
"AL",
"AM",
"AR",
"AT",
"AU",
"BA",
"BE",
"BG",
"BH",
"BO",
"BR",
"BY",
"CA",
"CH",
"CL",
"CO",
"CR",
"CY",
"CZ",
"DE",
"DK",
"DO",
"EC",
"EE",
"EG",
"ES",
"FI",
"FR",
"GB",
"GE",
"GR",
"GT",
"HN",
"HR",
"HU",
"ID",
"IE",
"IL",
"IN",
"IQ",
"IS",
"IT",
"JO",
"JP",
"KR",
"KW",
"LB",
"LT",
"LU",
"LV",
"MA",
"MD",
"ME",
"MK",
"MT",
"MX",
"MY",
"NI",
"NL",
"NO",
"NZ",
"OM",
"PK",
"PA",
"PE",
"PH",
"PL",
"PT",
"PY",
"PS",
"QA",
"RO",
"RS",
"SA",
"SE",
"SG",
"SI",
"SK",
"SV",
"TH",
"TN",
"TR",
"TW",
"UA",
"US",
"UY",
"VE",
"VN",
"ZA"
],
"type": "string"
},
"minItems": 1
},
"operator": {
"enum": [
"in",
"not_in"
],
"type": "string"
}
}
},
{
"type": "object",
"required": [
"field",
"operator",
"values"
],
"properties": {
"field": {
"type": "string",
"const": "chat_id"
},
"values": {
"type": "array",
"items": {
"type": "string"
},
"minItems": 1
},
"operator": {
"enum": [
"in",
"not_in"
],
"type": "string"
}
}
}
]
},
{
"type": "object",
"required": [
"field",
"operator",
"values"
],
"properties": {
"field": {
"type": "string",
"const": "brand_id"
},
"values": {
"type": "array",
"items": {
"type": "string"
},
"minItems": 1
},
"operator": {
"enum": [
"in",
"not_in"
],
"type": "string"
}
}
}
]
},
"description": "Filter results using {field, operator, values}. Operators: 'in', 'not_in'. Fields: model_id, tag_id, topic_id, prompt_id, brand_id, country_code, chat_id. Multiple filters are AND'd together."
},
"end_date": {
"type": "string",
"format": "date",
"pattern": "^(?:(?:\\d\\d[2468][048]|\\d\\d[13579][26]|\\d\\d0[48]|[02468][048]00|[13579][26]00)-02-29|\\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\\d|30)|(?:02)-(?:0[1-9]|1\\d|2[0-8])))$",
"description": "End date in ISO format (YYYY-MM-DD)"
},
"dimensions": {
"type": "array",
"items": {
"enum": [
"prompt_id",
"model_id",
"tag_id",
"topic_id",
"date",
"country_code",
"chat_id"
],
"type": "string"
},
"description": "Break down results by one or more dimensions. prompt_id = individual search queries, model_id = AI search engine, tag_id = custom tags, topic_id = topic groupings, country_code = country, chat_id = individual AI chat/conversation. When set, each row includes the corresponding dimension object."
},
"project_id": {
"type": "string",
"description": "The project ID"
},
"start_date": {
"type": "string",
"format": "date",
"pattern": "^(?:(?:\\d\\d[2468][048]|\\d\\d[13579][26]|\\d\\d0[48]|[02468][048]00|[13579][26]00)-02-29|\\d{4}-(?:(?:0[13578]|1[02])-(?:0[1-9]|[12]\\d|3[01])|(?:0[469]|11)-(?:0[1-9]|[12]\\d|30)|(?:02)-(?:0[1-9]|1\\d|2[0-8])))$",
"description": "Start date in ISO format (YYYY-MM-DD)"
}
}
}