ChatGPT Plugin

Quanti IA

Query your marketing warehouse

Brand: Quanti

Category: Data & Analytics
Primary Subcategory: Marketing & Commerce Data Integration

Integration details

Description

Quanti Analytics connects ChatGPT to your marketing data warehouse (BigQuery, Snowflake, Redshift), giving you instant access to cross-channel advertising data through natural language. What you can do: Ask questions about your marketing performance and get SQL queries generated from your actual table schemas — no hallucinated column names Run read-only queries across 200+ connectors: Google Ads, Meta, TikTok, Amazon Ads, Microsoft Ads, Shopify, Piano Analytics, and many more Browse pre-built report catalogs to discover available metrics and dimensions for each connector Save useful analyses as reusable templates, personal or shared with your team Run Marketing Mix Modeling studies to measure each channel's real contribution to your KPI How it works: Connect your Quanti account via OAuth ChatGPT retrieves your project's actual data schema — including semantic definitions of every metric and dimension Ask your question in plain language, get accurate SQL, review it, and execute Data access is strictly read-only. Quanti never modifies your data warehouse. Only projects and datasets you have permission to access are available.

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Data & Analytics
Primary Subcategory: Marketing & Commerce Data Integration
Secondary Subcategories: None listed
Brand: Quanti
Access: Account required
First tracked: 2026-05-26
Tool count: 22
Market: US

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Primary Subcategory

Competing in ChatGPT Marketing & Commerce Data Integration

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Listed in alphabetical order

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Tools

22 tools agents can invoke

Creates a materialized view or stored procedure in the project's BigQuery data warehouse for data pre-aggregation. **When to use this tool:** - When the user needs to pre-aggregate data from multiple connectors (e.g., cross-channel marketing report) - When a query is too slow to run on-demand and benefits from materialization - When the user asks to "create a view", "save this as a table", "materialize this query" - When data must be sent to an ad platform (Meta, Google Ads…): the response's push_source is exactly the configure_push source **Naming rules (enforced):** - Target dataset MUST be 'quanti_agg' (created automatically if it doesn't exist) - Object name MUST start with 'llm_' prefix (e.g., llm_weekly_spend) - Format: CREATE MATERIALIZED VIEW quanti_agg.llm_name AS SELECT ... **SQL format:** - CREATE MATERIALIZED VIEW: for pre-computed aggregation tables - CREATE OR REPLACE MATERIALIZED VIEW: to update an existing view - CREATE PROCEDURE: for complex multi-step transformations **Example:** CREATE MATERIALIZED VIEW quanti_agg.llm_weekly_channel_spend AS SELECT DATE_TRUNC(date, WEEK) as week, channel, SUM(spend) as total_spend FROM prod_google_ads_v2.campaign_stats GROUP BY 1, 2 **Semantic schema (REQUIRED):** You MUST describe every output column of the view in the 'columns' array. This schema is what powers the semantic layer: it lets future queries (yours and the user's) understand the view without re-reading the SQL, and it surfaces the schema in the Quanti UI. Be precise: - List ONE entry per column produced by the SELECT, using the exact output alias as 'name'. - Give each column a short business 'description' (what the value means, its unit if any). - Set 'is_metric: true' for numeric aggregates you can SUM/AVG (spend, clicks, revenue...), and false for dimensions you GROUP BY (date, channel, country...). - Also describe the view as a whole via 'description', 'purpose' and 'business_domain'. **Limits:** Maximum 20 active aggregation views per project.
create_aggregation_view
Permanently deletes a use case you created. **When to use this tool:** - When the user explicitly asks to delete a use case - To clean up obsolete or duplicate use cases **Warning:** This action is irreversible. The use case will be permanently deleted. **Permissions:** You can only delete use cases you created. **Tip:** Ask for user confirmation before deleting.
delete_use_case
Executes a read-only SQL SELECT query on the project's BigQuery data warehouse. No data modification allowed. **Table format**: Use `dataset.table` (e.g., `prod_google_ads_v2.campaign_stats`). Do NOT prefix with a project_id.
execute_query
Finds use cases: reusable analyses (SQL template + business definition) or guided setup journeys (category 'Activation': step-by-step instructions chaining several Quanti tools, e.g. sending consented conversions to Google Ads or Meta). Two modes: - **Search** (with question): the use cases relevant to a need — Quanti's, the user's own, and the project team's when project_id is given. Call it to discover available analyses, and FIRST whenever the user wants to set up or send something. - **List** (without question): every use case of a scope, with its ID — scope='member' (default) for the user's saved analyses, scope='project' for those shared with the team (requires project_id). Use it for "my analyses", before saving one to avoid duplicates, or to find the ID to update or delete.
get_use_cases
Récupère la spec ET les queries ACTUELLES d'un dashboard, et son template. À appeler AVANT de modifier un dashboard : lis la spec, modifie-la, renvoie la version complète à save_dashboard.
get_dashboard
Gets the full context of a project: active connectors with their datasets, visual branding, and business context (about both the client organization and the user querying the MCP). Use the folderId obtained from list_projects. Always call this tool before answering analytical questions or generating a report. **Response structure:** - `connectors`: active data sources with sku, dataset, last_sync, tables_count. May also carry `description`/`purpose` (account-level business notes) and, for custom connectors, `domains` (the business domains its tables cover, e.g. "CRM / Clients", "Ventes / E-commerce") and `tables` (client-defined table names). Use `domains`/`tables` to route to the right connector BEFORE calling get_schema_context — they tell you what an opaquely-named custom dataset actually contains. - `branding` (optional): visual identity (logo_url, primary_color, secondary_color, tertiary_color, font_family) — apply it when generating reports. - `organization` (optional): semantic info about the client company filled in by them during onboarding — `presentation` (what the company does), `industry` (sector), `culture` (working style). Use it to adapt tone, vocabulary, and analytical priorities. - `user` (optional): info about the human querying the MCP — `service` (department), `mission` (their role's objective), `role` (job title). Use it to tailor responses to their job context. Not present when authenticated via API key. - `context_summary` (optional): a ready-to-inject markdown summary of organization + user. Useful as a system prompt prefix. **Important:** `organization`, `user`, and `context_summary` are soft hints — they are absent for projects where the client hasn't completed the onboarding wizard. Treat their content as guidance, never as constraints.
get_project_context
Step 1 of schema discovery: returns the catalog of tables relevant to the user's question. Each table comes with its dataset, business name, dw_table_name and a short description — but NOT the field-level details (no columns, no types, no semantic codes). Use the catalog to identify the most promising candidate(s), then call **get_table_schema** to fetch the full structure of a specific table before writing SQL. **IMPORTANT for SQL queries**: Use ONLY the `dataset.table` format (e.g., `prod_google_ads_v2.campaign_stats`). NEVER prefix with a project_id.
get_schema_context
Step 2 of schema discovery: returns the full structure of a single table — fields with types, semantic codes, business names, and the semantic definitions referenced by those codes. Call this AFTER get_schema_context once you've picked a candidate table. Returns: dw_table_name (use this in SQL), description, fields[], semantic_definitions{} keyed by code.
get_table_schema
Invite une personne (par email) à consulter un dashboard. Réservé à l'owner. L'invité reçoit un email d'invitation et voit aussi l'invitation sur sa home Quanti. Utilise list_dashboards pour trouver l'id.
invite_to_dashboard
Lists aggregation views (materialized views and procedures) created for a project. **When to use this tool:** - When the user asks "what views exist?", "my aggregations", "my materialized views" - Before creating a new view to check it doesn't already exist - To get the view ID for deletion **Response format:** Returns a JSON array with each view's ID, full_name (dataset.name), type, SQL, description, and creation date.
list_aggregation_views
Lists the catalogue of standardized reports Quanti maintains for a given connector (also called "prebuilds" inside Quanti). **What is one of these reports?** It is a standardized report maintained by Quanti for a given connector (e.g., Campaign Stats for Google Ads). It defines the BigQuery table structure (columns, types, metrics) and the associated API query. For a REVERSE connector, there is no report and no table: the templates are outbound push contracts (which fields the third-party API expects). The response shape differs accordingly — see Response format below. **When to use this tool:** - When the user asks "what reports are available for [connector]?" - When the user doesn't know which data or metrics exist for a connector - BEFORE get_schema_context, to explore available reports for a connector - To understand the data structure before writing SQL **Difference with get_schema_context:** - list_connector_reports → discover which reports/tables EXIST for a connector (catalog) - get_schema_context → get the actual BigQuery schema for the client project (effective data) **Response format — TWO possible shapes, check which one you got:** 1. Inbound connectors (the usual case) → key **"prebuilds"**. For each: ID, name, description, BigQuery table name, and fields (name, type, description, is_metric). Fields with is_metric=true are aggregatable metrics (impressions, clicks, cost...), the others are dimensions (date, campaign_name...). These tables exist in the warehouse: you can write SQL against them. 2. Reverse connectors (SKU starting with "reverse-", e.g. reverse-googleads, reverse-meta) → key **"pushes"** and kind="reverse_pushes". These connectors SEND data OUT to a third-party platform (Google Ads, Meta...). Each push lists the destination fields to be FED, with: field_id, type, required, is_primary_key, and the transforms Quanti applies (auto_hash for PII hashed in SHA256, auto_normalize_phone for E.164). ⚠️ These field_id are NOT warehouse columns — no table exists behind them. Never write SQL against them. Use them to answer "what do I need to provide to feed this destination?", to help build the source table, or to explain a mapping. The mapping itself (destination field → source column) is configured in the connector's Mapping tab. **SKU examples**: googleads, meta, tiktok, tiktok-organic, amazon-ads, amazon-dsp, piano, shopify-v2, microsoftads, prestashop-api, mailchimp, kwanko — and for reverse: reverse-googleads, reverse-meta, reverse-meta-pixel, reverse-tiktok, reverse-amazonads, reverse-adobe
list_connector_reports
Liste les dashboards du projet auxquels l'utilisateur a accès (id, titre, url) ET les templates (chartes graphiques) existants (slug, nom, is_default, spec). ⚠️ APPELLE-LE AVANT toute création ou modification, avec include_guide=true : le guide donne le vocabulaire des blocs et le CONTRAT DONNÉES (forme tabulaire que ton SQL doit renvoyer par type de bloc), sans lequel la spec sera rejetée. Réutilise un template existant (son slug dans save_dashboard) ou modifie-le (save_template) ; n'en crée un nouveau que si la liste est vide.
list_dashboards
Lists all projects accessible by the user. Call this function first to discover available projects.
list_projects
Lists scheduled queries configured in the project's BigQuery. **What is a scheduled query?** A scheduled query is a SQL query automatically executed on a defined schedule in BigQuery. It is used to aggregate data, populate reporting tables, or perform recurring transformations. **When to use this tool:** - When the user EXPLICITLY asks about scheduled queries, BigQuery pipelines, or transfer configs - When the user asks "what are my scheduled queries?", "my BigQuery pipelines" - To check execution frequency or status of a specific scheduled query **Do NOT use this tool** for general data queries, analytics, or when the user asks about page views, metrics, or data from connectors. Use get_schema_context + execute_query instead. **Available filters:** - dataset: filter by destination dataset (e.g., 'prod_reports') - status: filter by status 'active' (enabled) or 'disabled' **Response format:** Returns a JSON with for each scheduled query: its name, SQL query, execution schedule, destination dataset, status (active/disabled), and last/next execution dates.
list_scheduled_queries
Re-runs a Marketing Mix Modeling study previously configured with setup_marketing_mix_model. **Important:** Do NOT call this right after setup_marketing_mix_model. The first run is automatically triggered by setup_marketing_mix_model. Use run_marketing_mix_model only to re-launch an existing study later (e.g., after data refresh or parameter changes). **Prerequisite:** Must have called setup_marketing_mix_model first to obtain an account_id. **Duration:** The Meridian fit (MCMC) takes approximately 10-30 minutes depending on data volume. The user will receive an email when results are ready. **Results:** Results are written to the project's data warehouse (mmm_channel_summary and mmm_weekly_contributions tables). They can then be queried via execute_query.
run_marketing_mix_model
Crée un dashboard, le modifie, ou estime son coût, à partir d'une spec déclarative Quanti (pages, blocs : heading/text/callout/section/grid/kpi_band/kpi_card/chart/table) et de ses bindings de données (queries clé→SQL). N'émets JAMAIS de HTML ni de Chart.js : uniquement la spec JSON. - **Estimer** (dry_run=true, queries requis) : octets BigQuery scannés et coût, sans rien exécuter ni créer. À faire AVANT la création pour informer l'utilisateur du coût récurrent. - **Créer** (sans dashboard_id) : title, slug et spec requis. - **Modifier** (avec dashboard_id) : garde l'id, l'URL et le partage. Remplace la spec (et title/template/queries si fournis) : get_dashboard d'abord, renvoie la spec COMPLÈTE. Réservé à l'owner/editor.
save_dashboard
Crée ou modifie un template de dashboard (charte réutilisable : couleurs, typo, logo, header/footer), identifié par son slug dans le projet. ⚠️ Si un template porte déjà ce slug, il est MODIFIÉ et tous les dashboards qui l'utilisent récupèrent la nouvelle charte : c'est ce qu'il faut pour toute modification graphique (slug du template en cours, lu via get_dashboard). Un nouveau slug crée un nouveau template, réservé à une NOUVELLE palette — propose alors les couleurs à l'utilisateur. Fournis la spec COMPLÈTE (format dans le guide de list_dashboards).
save_template
Saves a use case (reusable analysis): creates it, or updates it when use_case_id is given. **When to use this tool:** - When the user asks to "save this analysis", "create a use case", "remember this query" - After building a SQL query the user wants to reuse - To improve, fix or recategorize an existing use case (pass its use_case_id; only the fields given change) **Available scopes (creation only):** - 'member' (default): Personal use case, visible only to you - 'project': Shared with the entire project team (requires project_id) **Permissions:** You can only update use cases you created. **Best practices:** - Call get_use_cases (list mode) first: to avoid a duplicate, and to get the ID of the one to update - Slug: technical identifier in snake_case (e.g., weekly_campaign_performance) - Name: human-readable name (e.g., "Weekly Campaign Performance") - Description: explain the business context and when to use this analysis - SQL template: include the SQL query if it's generic and reusable
save_use_case
Searches the official Quanti documentation (docs.quanti.io) to answer questions about using the platform. **When to use this tool:** - When the user asks "how to do X in Quanti?", "what is a connector?", "how to configure BigQuery?" - When the user needs help configuring or using a connector (Google Ads, Meta, Piano, etc.) - To explain Quanti concepts: projects, connectors, prebuilds, data warehouse, tag tracker, transformations - When the user asks about the Quanti MCP (setup, overview, semantic layer) **This tool does NOT replace:** - get_schema_context: to get the actual BigQuery schema for a client project - list_connector_reports: to list the standardized reports available for a connector - get_use_cases: to find reusable analyses - execute_query: to execute SQL **Available topic filters:** connectors, data-warehouses, data-management, tag-tracker, mcp-server, transformations
search_documentation
Configures a Marketing Mix Modeling (MMM) study for a project. **What is MMM?** Marketing Mix Modeling measures the real contribution of each marketing channel (Google Ads, Meta, etc.) on a KPI (leads, revenue, conversions), accounting for external factors (seasonality, holidays, promotions). **Recommended workflow:** 1. Use get_schema_context to discover the project's tables/columns 2. Generate input SQL queries (KPI, channels, exogenous variables) 3. **Validate each query before calling setup_marketing_mix_model:** Use execute_query to run a COUNT(*) wrapper on each input query (e.g., SELECT COUNT(*) FROM (<query>)). If any query returns 0 rows, do NOT include it in setup_marketing_mix_model — warn the user that the data source is empty and ask whether to proceed without it or fix the query. 4. Call setup_marketing_mix_model with the validated SQL queries — the study is automatically launched after setup 5. Do NOT call run_marketing_mix_model after setup_marketing_mix_model: the first run is triggered automatically **Important:** run_marketing_mix_model is only needed to RE-RUN an existing study later, not after initial setup. **Input queries format:** Each query must return a "time" column (DATE) and the requested metrics. - role="kpi": a "kpi" column (the target KPI) - role="channel": "spend" and "impressions" columns + channel_name - role="exogenous": columns named after the exogenous variables + columns[] **Granularity**: "weekly" is recommended (MMM standard). SQL should aggregate by week. **Important**: Adapt the SQL dialect to the project's data warehouse type (BigQuery, Snowflake, Redshift).
setup_marketing_mix_model
Crée un connecteur (pull ou reverse) pas à pas, via une session de setup Quanti. Appeler avec `action`, dans cet ordre : 1. `start` (sku) : ouvre la session et renvoie un `connect_url`. Les credentials (clé d'API, OAuth) ne passent JAMAIS par la conversation : donne ce lien à l'utilisateur, il s'y connecte à Quanti et autorise l'accès, puis revient. Ne lui demande JAMAIS de coller une clé ici. 2. `status` (session_id) : où en est la session et ce qu'il faut faire ensuite (`next`) ; renvoie les comptes à choisir quand c'est le moment. 3. `select_account` (account_id) puis, si demandé, `select_sub_accounts` (accounts) ; pour un connecteur pull, `select_templates` (template_ids). 4. `create` (account_name, sync_hour, lookback_days) : crée le connecteur. Pour un reverse, sync_hour doit être APRÈS l'heure de fin des connecteurs qui alimentent la source (sinon le push lit une table vide, run vert, rien envoyé) ; lookback_days = 2 pour Meta Pixel et GA4 (dédoublonnage 48 h), 7 sinon. Webhook à schéma fixe (ex. webhook-tag-analytics) : `start` puis `create` avec account_name seul (ni credentials, ni synchro) ; renvoie webhook_url et uniq_id, à mettre dans le tag. Un webhook qui déduit son schéma d'un payload de test se crée depuis l'app Quanti. Les pushes d'un reverse se configurent ENSUITE avec configure_push. Écriture : indisponible en mode clé API.
setup_connector
Unregisters an aggregation view from Quanti: it stops being listed, stops being used by the semantic layer, and frees one of the project's 20 slots. **IMPORTANT — this does NOT drop the BigQuery object.** The materialized view or procedure keeps existing in the data warehouse, keeps being refreshed, and keeps incurring BigQuery costs until someone drops it manually. Tell the user this plainly whenever you call this tool; do not report the view as deleted from their warehouse. **When to use this tool:** - When the user explicitly asks to remove a view from Quanti - To clean up unused or obsolete aggregations - When the project has reached the maximum number of views (20) **Tip:** Use list_aggregation_views first to get the view ID.
unregister_aggregation_view

FAQs

Quanti IA ChatGPT Plugin FAQ

How the directory, categories and Discoverability Score work.

Read the methodology

How do I improve Quanti IA's ChatGPT Plugin discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

What are Quanti IA alternatives on ChatGPT?

As of 2026-11-03, Quanti IA competes with Adzviser, Adzviser Amazon Ads, Adzviser CallRail, Adzviser CallTrackingMetrics, Adzviser Klaviyo, Adzviser Snapchat Ads, Catchr, Converly and 15 more in ChatGPT Marketing & Commerce Data Integration, ranked by public Discoverability Score.

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.