{"id":4021,"external_id":"plugin_asdk_app_6a5f9d9c29d08191a0d20fa7681578b1","name":"app-6a5f9d9c29d08191a0d20fa7681578b1","display_name":"Revenue Analytics","developer":"DataLabs.store","category":"Business & Operations","listing_language":"en","listing_language_details":{"method":"cld-0.13.0/listing-v1","reliable":true,"detected_at":"2026-10-01T13:22:03Z","input_sha256":"4d7d524c835dbb1bf212632ee37c65923809934de1554333775db84599b9164c","source_fields":["release.description","release.interface.short_description","release.interface.long_description"]},"version":"1.0.1","skill_count":1,"first_seen_at":"2026-09-30T22:02:35.000Z","last_seen_at":"2026-10-03T18:00:01.200Z","last_changed_at":"2026-09-30T22:02:35.000Z","install_count":null,"search_matches":[],"research_summary":null,"research_reviewed_at":null,"metadata":{"canonical_app_id":"asdk_app_6a5f9d9c29d08191a0d20fa7681578b1","connector_id":"asdk_app_6a5f9d9c29d08191a0d20fa7681578b1","created_at":"2026-07-21T16:26:04.301516Z","discoverability":"LISTED","id":"plugin_asdk_app_6a5f9d9c29d08191a0d20fa7681578b1","is_template":false,"name":"app-6a5f9d9c29d08191a0d20fa7681578b1","release":{"app_ids":["asdk_app_6a5f9d9c29d08191a0d20fa7681578b1"],"app_manifest":{"apps":{"app-6a5f9d9c29d08191a0d20fa7681578b1":{"id":"asdk_app_6a5f9d9c29d08191a0d20fa7681578b1","required":true}}},"app_templates":[],"description":"Revenue Analytics by DataLabs.store connects your HubSpot data to ChatGPT. It continuously replicates your portal (custom objects, deals, contacts, companies, line items, engagements, email events) into a real relational Microsoft SQL Server database whose schema matches your portal, then lets ChatGPT query it in plain English and return answers backed by the exact SQL that produced them.\n\nWhy it exists\nHubSpot is built to run your CRM at scale, and its reporting tools are genuinely good at what they were designed for. The limit is structural: standard reports are built one object at a time, so questions that need several tables joined together before the math even starts do not fit. Per-deal margin (deals joined to their line items), multi-hop paths (deal to contact to company to source), correctly weighted stats (SUM(clicks)/SUM(delivered), not an average of averages), cohorts, and window functions all need a real database underneath. That is the job this fills.\n\nWhat you can ask\n- \"Rank owners by weighted pipeline (each deal's amount times its real stage odds) and their historical win rate.\"\n- \"Unique click-through rate by acquisition source, weighted by delivered volume, for contacts who became customers.\"\n- \"Which deals are aging past 90 days, and where does our ARR actually concentrate?\"\nThen keep drilling: \"now split by pipeline... now only Q2... now show the trend.\"\n\nWhat makes it different\n- Real joins across any objects, expressed as one query.\n- Every answer is auditable: the exact SQL ships with the number, so you can verify and reproduce it.\n- You bring the model: full frontier ChatGPT reasoning runs over your own data.\n- Complementary to HubSpot's own AI. Breeze works inside the CRM; this understands the data behind it.\n\nA note on sensitive data\nThis app's HubSpot integration requests only standard, non-sensitive OAuth read scopes for CRM analytics. It does not request HubSpot's sensitive scope grants (for example, crm.objects.contacts.sensitive.read). Because HubSpot enforces this at the API level, any property a customer has explicitly marked as a \"sensitive data\" property inside their own HubSpot account is withheld by HubSpot and never delivered to our sync process. It cannot enter the customer's synced database, and therefore cannot be returned by any of this MCP's tools, regardless of the query executed.","display_name":"Revenue Analytics","id":"pluginrel_7f2133a0d93c8191bfa9686db8e00f86","interface":{"brand_color":null,"capabilities":[],"category":"Business & Operations","composer_icon_dark_url":"https://files.openai.com/content?id=file_00000000b93481f79c3093209b23803a","composer_icon_url":"https://files.openai.com/content?id=file_00000000b93481f79c3093209b23803a","default_prompt":"Which emails actually turn prospects into deals - and how fast","default_prompts":["Which emails actually turn prospects into deals - and how fast","For each pipeline stage average days a deal sits there, and how many activities we log while it's there","Which rep has the best pipeline once you account for who actually closes?"],"developer_name":"DataLabs.store","logo_url":"https://files.openai.com/content?id=file_00000000f9708211ab3b331d182e0e99","logo_url_dark":"https://files.openai.com/content?id=file_00000000c5dc8211b7829f01ac860e89","long_description":"Revenue Analytics by DataLabs.store connects your HubSpot data to ChatGPT. It continuously replicates your portal (custom objects, deals, contacts, companies, line items, engagements, email events) into a real relational Microsoft SQL Server database whose schema matches your portal, then lets ChatGPT query it in plain English and return answers backed by the exact SQL that produced them.\n\nWhy it exists\nHubSpot is built to run your CRM at scale, and its reporting tools are genuinely good at what they were designed for. The limit is structural: standard reports are built one object at a time, so questions that need several tables joined together before the math even starts do not fit. Per-deal margin (deals joined to their line items), multi-hop paths (deal to contact to company to source), correctly weighted stats (SUM(clicks)/SUM(delivered), not an average of averages), cohorts, and window functions all need a real database underneath. That is the job this fills.\n\nWhat you can ask\n- \"Rank owners by weighted pipeline (each deal's amount times its real stage odds) and their historical win rate.\"\n- \"Unique click-through rate by acquisition source, weighted by delivered volume, for contacts who became customers.\"\n- \"Which deals are aging past 90 days, and where does our ARR actually concentrate?\"\nThen keep drilling: \"now split by pipeline... now only Q2... now show the trend.\"\n\nWhat makes it different\n- Real joins across any objects, expressed as one query.\n- Every answer is auditable: the exact SQL ships with the number, so you can verify and reproduce it.\n- You bring the model: full frontier ChatGPT reasoning runs over your own data.\n- Complementary to HubSpot's own AI. Breeze works inside the CRM; this understands the data behind it.\n\nA note on sensitive data\nThis app's HubSpot integration requests only standard, non-sensitive OAuth read scopes for CRM analytics. It does not request HubSpot's sensitive scope grants (for example, crm.objects.contacts.sensitive.read). Because HubSpot enforces this at the API level, any property a customer has explicitly marked as a \"sensitive data\" property inside their own HubSpot account is withheld by HubSpot and never delivered to our sync process. It cannot enter the customer's synced database, and therefore cannot be returned by any of this MCP's tools, regardless of the query executed.","plugin_category_id":"business & operations","privacy_policy_url":"https://datalabs.store/privacy-notice","screenshot_urls":[],"short_description":"Non-trivial RevOps reports","terms_of_service_url":"https://datalabs.store/conditions-of-use","website_url":"https://datalabs.store/ai-context-bridge-for-hubspot"},"keywords":[],"onboarding_skill_name":null,"requires_local_executor":false,"skills":[{"description":"Use when the user has connected DataLabs' \"Revenue Analytics\" MCP connector (mcp.datalabs.store) - either as a custom connector exposing tools like execute_query/get_tables_list/get_full_database_schema/get_semantic_metadata/save_query/list_saved_queries, or as a Company Knowledge source exposing search/fetch - and asks analytical questions about their HubSpot data (deals, pipeline, contacts, companies, engagements, email campaigns, workflows). Also use for schema exploration, SQL generation/debugging against this connector, or reproducing known revenue-ops analyses (weighted pipeline, forecast calibration, stuck deals, engagement-vs-win-rate, revenue concentration, email attribution).","interface":{"brand_color":null,"default_prompt":null,"display_name":"revenue-analytics","icon_large_url":null,"icon_small_url":null,"iconography":"chart","short_description":"Use when the user has connected DataLabs' \"Revenue Analytics\" MCP connector (mcp.datalabs.store) - either as a custom connector exposing tools like execute_query/get_tables_list/get_full_database_schema/get_semantic_metadata/save_query/list_saved_queries, or as a Company Knowledge source exposing search/fetch - and asks analytical questions about their HubSpot data (deals, pipeline, contacts, companies, engagements, email campaigns, workflows). Also use for schema exploration, SQL generation/debugging against this connector, or reproducing known revenue-ops analyses (weighted pipeline, forecast calibration, stuck deals, engagement-vs-win-rate, revenue concentration, email attribution)."},"name":"revenue-analytics","plugin_release_skill_id":"pluginrsk_6a993a70abb481918bfda3cc76ed7abf"}],"version":"1.0.1"},"scope":"GLOBAL","status":"ENABLED"},"research":null,"package_metadata":{"name":"app-6a5f9d9c29d08191a0d20fa7681578b1","author":{"name":"DataLabs.store"},"sources":[{"path":".codex-plugin/plugin.json","sha256":"45318ad012715077601c333600b66f43954a174feb059a215e6be378e5ea626e"}],"version":"1.0.1","artifact_id":16570,"observed_at":"2026-10-03T06:29:15Z","support_url":"https://datalabs.store/contactus","field_sources":{"name":0,"author":0,"version":0,"support_url":0},"extraction_version":1,"conflicts_or_errors":[]}}