{"apiVersion":"1","solution":{"slug":"mcp-igaming-analytics","title":"iGaming Analytics Platforms with MCP Access","summary":"Connect an external AI workspace to your analytics. Compare what the product exposes through MCP, whose permissions apply and how to check an answer against its source.","reviewedAt":"2026-10-10","canonicalUrl":"https://igamingdiscovery.com/solutions/mcp-igaming-analytics","jsonUrl":"https://igamingdiscovery.com/api/discovery/v1/solutions/mcp-igaming-analytics","operator":"AND","capabilityIds":["mcp-analytics-access"],"relatedTypes":["business-intelligence","conversational-analytics","data-warehouses"],"answer":"Model Context Protocol access can let an external AI application retrieve analytics from a connected product. This selection requires product-specific documentation of customer access to metrics, dashboards, reports or insights through MCP. A conventional API, developer toolkit or internal use of MCP does not establish that connection for your team.","qualification":["Official product material explicitly describes Model Context Protocol access for a customer’s external AI tool or client, rather than mentioning MCP only within the supplier’s architecture.","The connection exposes analytics from that named product: metrics, dashboards, reports or insights. A generic server framework or an unrelated product’s connector does not qualify.","The capability must have documented status in the current review. Conditional, expired or missing evidence does not qualify; absence from this selection is not proof that access is unavailable."],"caveats":["MCP support does not establish compatibility with every AI application, account plan or deployment. Confirm the supported client, connection method, administrator settings and analytics objects you need.","Analytics access does not establish permission to change campaigns, issue bonuses, alter player accounts or place bets. Confirm the exposed tools and any write operations separately.","An external AI client introduces another data-handling boundary. Confirm what leaves the analytics platform, where the client processes or retains it and how access can be withdrawn."],"sections":[{"id":"external-access","title":"Confirm that your own AI client can reach the analytics","paragraphs":["Start with an analyst connecting an approved AI application to the analytics product and asking an operational question. MCP defines an exchange between clients and servers; it does not establish which customer data a supplier exposes. Ask for setup instructions, available analytics tools or resources, and a demonstration from your intended client.","The distinction matters when reading architecture claims. DigerAnalytics describes MCP between its AI and data layers, which alone does not document an external customer connection. Gamblitude’s AI and BI product pages explicitly describe external queries of Metrics, Dashboards and insights through its MCP Server. Evaluate the documented connection for the named product, without transferring access to other products in the supplier’s portfolio."],"sourceIds":["mcp-architecture","diger-mcp-boundary","gamblitude-ai-mcp","gamblitude-bi-mcp"]},{"id":"permissions-and-data","title":"Follow the user’s permissions through the connection","paragraphs":["Use two test accounts with different access to brands, dashboards or sensitive information. Request the same analysis through each account, then compare the returned scope with the analytics interface. Gamblitude states that existing permissions apply and that a Settings Admin controls connection enablement. Treat those statements as a basis for an acceptance test, not as evidence that you have tested the controls yourself.","Ask which identity the server sees, what the connection authorises and who can revoke it. The MCP authorization specification describes restricted server access, but does not prove a product’s implementation. Map the returned data and the external application’s processing arrangements. A platform’s hosting policy cannot answer where an external client stores conversations or handles results."],"sourceIds":["gamblitude-ai-mcp","gamblitude-bi-mcp","mcp-authorization"]},{"id":"reproducible-answers","title":"Make one analytical answer reproducible before expanding usage","paragraphs":["Choose a question with a known answer, such as the change in deposits for one brand over a specified period. Record the metric, filters, currency, timezone and data refresh point. Compare the returned figures with the equivalent platform view, then ask a follow-up that changes only one filter. Request enough context to identify what was retrieved and what the external model added as interpretation.","Inspect the available tools: a successful connection does not establish access to every dashboard or report. MCP specifies tool discovery and descriptions; analytical scope remains product-specific. Test an inaccessible dashboard, an expired connection and an unsupported question. Agree how failures, incomplete results and tool changes are presented before relying on recurring analysis."],"sourceIds":["mcp-tools","gamblitude-bi-mcp"]}],"questions":[{"id":"client-and-scope","question":"Which external clients and analytics objects can our users connect to?","why":"Request current setup instructions and a demonstration using your intended client. Identify the available metrics, dashboard queries, reports and insights, plus any account or deployment prerequisites.","requirement":"Demonstrate our chosen external MCP client and list the supported analytics objects, connection method and prerequisites.","sourceIds":["gamblitude-ai-mcp","gamblitude-bi-mcp","mcp-architecture"]},{"id":"access-boundaries","question":"Does the connection preserve each user’s analytical access boundaries?","why":"Compare restricted and broader accounts against the same request. Show who enables the connection, how identity is mapped and what happens when an administrator removes access.","requirement":"Test MCP access with different user permissions and demonstrate administrator enablement and effective access revocation.","sourceIds":["gamblitude-ai-mcp","mcp-authorization"]},{"id":"answer-context","question":"Can we reconcile a returned answer with the original platform view?","why":"Use an agreed metric and period, then change a filter. Request the source object, applied parameters and refresh context, distinguishing returned data from commentary generated by the external model.","requirement":"Reconcile an MCP answer with the platform using matching metrics, filters, periods, currency and data refresh context.","sourceIds":["gamblitude-bi-mcp","mcp-tools"]},{"id":"external-data-handling","question":"What data reaches the external application, and under whose policies?","why":"Trace one request and its response across the platform, connector and client. Confirm credentials, retention arrangements and permitted data categories for the exact client configuration you will deploy.","requirement":"Document the MCP data path, external processing and retention arrangements, credential handling and permitted data categories.","sourceIds":["mcp-architecture","mcp-authorization"]},{"id":"tool-boundaries","question":"How do we inspect the tool scope and handle unavailable analysis?","why":"Show the available tools and identify any writes separately from analytical retrieval. Demonstrate denied access and unsupported requests, then explain change notifications, support ownership and recovery.","requirement":"List exposed MCP tools, distinguish reads from writes and demonstrate unsupported requests, denied access and connection recovery.","sourceIds":["mcp-tools","diger-mcp-boundary"]}],"sources":[{"id":"gamblitude-ai-mcp","label":"Gamblitude — AI for iGaming: external MCP connection","url":"https://gamblitude.ai/products/ai-for-igaming/","publisher":"Gamblitude","checkedAt":"2026-10-10","supports":"Describes querying Metrics, Dashboards and insights from external AI tools through the Gamblitude MCP Server, with existing user permissions and Settings Admin enablement."},{"id":"gamblitude-bi-mcp","label":"Gamblitude — Business Intelligence Platform: MCP access","url":"https://gamblitude.ai/products/business-intelligence-platform-for-igaming/","publisher":"Gamblitude","checkedAt":"2026-10-10","supports":"The named BI product explicitly describes external access to Metrics, Dashboards and insights through MCP and identifies permission and connection controls."},{"id":"diger-mcp-boundary","label":"Diger — DigerAnalytics architecture","url":"https://diger.io/analytics","publisher":"Diger","checkedAt":"2026-10-10","supports":"Describes MCP between the product’s AI and data layers. This is architectural context, not evidence of customer access from an external MCP client."},{"id":"mcp-architecture","label":"Model Context Protocol — Architecture overview","url":"https://modelcontextprotocol.io/docs/2026-07-28/learn/architecture","publisher":"Model Context Protocol","checkedAt":"2026-10-10","supports":"Protocol context for hosts, clients, servers and context exchange. Does not establish any listed product’s implementation or supported analytics."},{"id":"mcp-authorization","label":"Model Context Protocol — Authorization","url":"https://modelcontextprotocol.io/specification/2026-07-28/basic/authorization","publisher":"Model Context Protocol","checkedAt":"2026-10-10","supports":"Protocol context for client authorization to restricted HTTP servers, scopes and access errors. Product permissions and data-handling arrangements require separate verification."},{"id":"mcp-tools","label":"Model Context Protocol — Tools","url":"https://modelcontextprotocol.io/specification/2026-07-28/server/tools","publisher":"Model Context Protocol","checkedAt":"2026-10-10","supports":"Protocol context for tool discovery, descriptions, invocation and changing availability; does not prove a supplier exposes particular reads or writes."}],"requirements":[{"id":"mcp-analytics-access","label":"Analytics access through MCP","group":"Data access","definition":"Customer-enabled external AI tools can use a documented Model Context Protocol connection to read analytical data, metrics, dashboards, reports or insights from the named product. An ordinary API, developer SDK or internal use of MCP alone does not qualify. Supported objects, permissions and setup require confirmation; this does not establish write access or business-action execution.","aliases":["MCP analytics server","Model Context Protocol analytics"],"requirement":"A documented MCP connection for external AI tools to read the product’s analytical data or outputs, with scope and permissions confirmed.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"]}]},"capabilityEvidenceMeaning":"Documented means supported by the cited supplier material, not independently tested. Unknown is not a negative finding. Conditional capabilities do not count as strict matches. Evidence applies to the named product only; it does not establish live integrations, contractual availability or measured outcomes.","capabilitySearch":{"vocabularyVersion":"2026-10-10.4","scope":"data-ai","operator":"AND","selected":["mcp-analytics-access"],"mode":"documented","eligibleProducts":33,"reviewedProducts":33,"documentedCount":2,"confirmationCount":31,"excludedCount":0,"facets":[{"id":"managed-data-warehouse","label":"Managed data warehouse","group":"Data foundation","definition":"The supplier operates a persistent analytical data warehouse for the customer. A dashboard, data feed or customer-managed database alone does not qualify.","aliases":["managed warehouse","managed DWH"],"requirement":"A supplier-operated analytical data warehouse, with hosting and operational responsibilities documented.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"event-level-history","label":"Historical event-level data","group":"Data foundation","definition":"Retained individual historical events or transactions can be analysed. Aggregated historical charts alone do not qualify; retention duration must be confirmed separately.","aliases":["transaction-level history","granular event history"],"requirement":"Analysis of retained event-level or transaction-level history, with retention and available fields confirmed.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"shared-metric-definitions","label":"Shared metric definitions","group":"Business metrics","definition":"A reusable governed metric or semantic layer supplies consistent definitions across analytical outputs. Merely listing KPI dashboards does not qualify.","aliases":["semantic layer","governed metrics","metric system"],"requirement":"Shared, governed metric definitions reused across reports and analytical outputs.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":2,"confirmationCount":31},{"id":"custom-metrics","label":"Custom business metrics","group":"Business metrics","definition":"The product supports defining business-specific calculated metrics or formulas. Customising a dashboard layout alone does not qualify.","aliases":["calculated metrics","custom KPIs"],"requirement":"The ability to define business-specific calculated metrics or formulas.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"natural-language-analytics","label":"Natural-language analytics","group":"AI analysis","definition":"Users ask analytical questions in natural language and receive answers grounded in data available to this product. A general support chatbot does not qualify.","aliases":["conversational analytics","ask your data","AI analyst"],"requirement":"Natural-language analytical questions answered using the data available to the product.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":2,"confirmationCount":31},{"id":"multi-step-investigations","label":"Multi-step investigations","group":"AI analysis","definition":"The product carries out a sequence of analytical steps to investigate a question, for example forming hypotheses and drilling into contributing factors. Single-query answers alone do not qualify.","aliases":["root cause investigation","multi-step analysis"],"requirement":"Multi-step analytical investigations with intermediate findings and contributing factors.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":0,"confirmationCount":33},{"id":"autonomous-investigations","label":"Autonomous investigations","group":"Autonomy","definition":"The product selects analytical topics or investigative paths and initiates investigations without a separate user question or predefined recurring check. Monitoring, events or a standing goal may provide context, but chat-driven analysis, a user-defined scheduled investigation, a threshold alert or a scheduled report alone does not qualify. This does not imply execution of external business actions.","aliases":["autonomous analyst","autonomous agent"],"requirement":"System-initiated analytical investigations with topic or path selection, beyond user-defined recurring checks, with initiation and scope documented.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"proactive-monitoring","label":"Proactive monitoring","group":"Autonomy","definition":"The product monitors data and surfaces detected changes, risks or anomalies without an ad hoc analytical query. A static dashboard or scheduled report alone does not qualify.","aliases":["anomaly monitoring","proactive alerts"],"requirement":"Monitoring that surfaces detected changes, risks or anomalies without an ad hoc query.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"scheduled-ai-investigations","label":"Scheduled AI investigations","group":"AI analysis","definition":"Users define an analytical question or investigation and schedule AI to repeat the analysis and return findings with context. A threshold alert, continuous risk score, dashboard subscription or recurring report alone does not qualify. This is distinct from an agent choosing its own investigative topics.","aliases":["recurring AI investigations","scheduled analytical checks"],"requirement":"User-defined analytical investigations repeated by AI on an agreed schedule, with findings and context returned for review.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":1,"confirmationCount":32},{"id":"mcp-analytics-access","label":"Analytics access through MCP","group":"Data access","definition":"Customer-enabled external AI tools can use a documented Model Context Protocol connection to read analytical data, metrics, dashboards, reports or insights from the named product. An ordinary API, developer SDK or internal use of MCP alone does not qualify. Supported objects, permissions and setup require confirmation; this does not establish write access or business-action execution.","aliases":["MCP analytics server","Model Context Protocol analytics"],"requirement":"A documented MCP connection for external AI tools to read the product’s analytical data or outputs, with scope and permissions confirmed.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":2,"confirmationCount":31},{"id":"ai-generated-reports","label":"AI-generated analytical reports","group":"AI analysis","definition":"The product generates an analytical report from a user brief or request, combining data-grounded written analysis with charts or other data visualisations in a report document. Static exports, templates, scheduled dashboard snapshots or isolated chat answers alone do not qualify. Scheduling, export formats and delivery channels require separate confirmation.","aliases":["AI-written reports","generative analytics reports"],"requirement":"Analytical reports generated from a brief, combining supporting data visualisations and written analysis for human review.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":2,"confirmationCount":31},{"id":"data-export-api","label":"Data export API","group":"Data access","definition":"A documented API lets the customer retrieve analytical data, reports or results from this product. An ingestion-only API, partner logo or unrelated supplier API does not qualify.","aliases":["reporting API","analytics export API"],"requirement":"API retrieval of analytical data, reports or results, with available fields and access conditions confirmed.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":0,"confirmationCount":33},{"id":"direct-sql-access","label":"Direct SQL access","group":"Data access","definition":"Customer users or tools can query product data using SQL through a documented query surface or database connection. SQL used only internally by the supplier does not qualify.","aliases":["SQL querying","SQL connection"],"requirement":"Customer-accessible SQL querying of analytical data, with permissions and access method documented.","scope":"data-ai","productTypeSlugs":["business-intelligence","data-warehouses","conversational-analytics","player-analytics","predictive-analytics"],"documentedCount":0,"confirmationCount":33}],"relaxations":[]},"items":[{"id":"12","slug":"gamblitude-ai-agent","name":"AI for iGaming","supplierSlug":"gamblitude","supplierName":"Gamblitude","canonicalUrl":"https://igamingdiscovery.com/products/gamblitude-ai-agent","productTypeSlugs":["conversational-analytics"],"summary":"AI-assisted business questions, monitoring and reports grounded in operator data.","capabilityReview":{"vocabularyVersion":"2026-10-10.4","scope":"product","reviewState":"reviewed","reviewedAt":"2026-10-10","reviewDueAt":"2027-04-08"},"capabilityMatch":{"status":"documented","operator":"AND","requested":["mcp-analytics-access"],"documented":["mcp-analytics-access"],"conditional":[],"unknown":[],"notSupported":[],"notApplicable":[],"requirements":[{"capabilityId":"mcp-analytics-access","label":"Analytics access through MCP","status":"documented","summary":"External AI clients can query Metrics, Dashboards and insights through the Gamblitude MCP Server, with administrator-controlled enablement and the user's existing permissions.","conditions":null,"sources":[{"id":"gamblitude-ai-mcp-access","url":"https://gamblitude.ai/products/ai-for-igaming/","title":"AI for iGaming — external MCP access","publisher":"Gamblitude","checkedAt":"2026-10-10","supports":"The named AI product describes external clients querying Metrics, Dashboards and insights through the Gamblitude MCP Server; existing permissions apply and a Settings Admin enables the connection."}]}]},"links":{"profile":"https://igamingdiscovery.com/products/gamblitude-ai-agent","evidence":"https://igamingdiscovery.com/api/discovery/v1/products/gamblitude-ai-agent"}},{"id":"11","slug":"gamblitude-business-intelligence","name":"Business Intelligence Platform","supplierSlug":"gamblitude","supplierName":"Gamblitude","canonicalUrl":"https://igamingdiscovery.com/products/gamblitude-business-intelligence","productTypeSlugs":["business-intelligence","player-analytics"],"summary":"Self-service dashboards, Segments, Reports and Targets for iGaming teams.","capabilityReview":{"vocabularyVersion":"2026-10-10.4","scope":"product","reviewState":"reviewed","reviewedAt":"2026-10-10","reviewDueAt":"2027-04-08"},"capabilityMatch":{"status":"documented","operator":"AND","requested":["mcp-analytics-access"],"documented":["mcp-analytics-access"],"conditional":[],"unknown":[],"notSupported":[],"notApplicable":[],"requirements":[{"capabilityId":"mcp-analytics-access","label":"Analytics access through MCP","status":"documented","summary":"The BI product exposes Metrics, Dashboards and insights to external AI tools through MCP; a Settings Admin controls the connection and existing user permissions govern access.","conditions":null,"sources":[{"id":"gamblitude-bi-mcp-access","url":"https://gamblitude.ai/products/business-intelligence-platform-for-igaming/","title":"Business Intelligence Platform — external MCP access","publisher":"Gamblitude","checkedAt":"2026-10-10","supports":"The BI product page explicitly describes customer AI workspaces querying Metrics, Dashboards and insights through MCP, with user permissions preserved and Settings Admin connection control."}]}]},"links":{"profile":"https://igamingdiscovery.com/products/gamblitude-business-intelligence","evidence":"https://igamingdiscovery.com/api/discovery/v1/products/gamblitude-business-intelligence"}}],"pagination":{"page":1,"pageSize":24,"total":2,"totalPages":1,"next":null,"previous":null}}