MCP in Analytics: Vendor Capabilities and Architectural Differences

4 min read | Published Jan 18, 2026

Written by Natalia Nanistova

The Model Context Protocol (MCP) provides a standardized way for AI systems to interact with external tools and services. In analytics, this means AI can work directly with BI platforms: reading data structures, executing queries, and in some cases, modifying analytical assets.

As organizations begin integrating AI agents into their analytics workflows, understanding what different platforms actually support through MCP becomes essential. Not all MCP implementations provide the same capabilities, and these differences have practical implications for how AI can participate in analytics work.

This document examines MCP support across major analytics platforms, focusing on what AI systems can and can’t do within each environment.

What MCP Can Enable in Analytics

MCP implementations in analytics typically support two categories of operations:

1) Building Analytics Infrastructure

Creating and modifying semantic models
Defining metrics and calculations
Generating dashboards and visualizations
Setting up alerts and automations
Deploying analytics configurations

2) Querying Analytics

Executing natural language queries against existing data
Generating insights and summaries
Creating ad-hoc visualizations
Exploring existing analytical content

The distinction matters because AI can only query analytics that already exist. If semantic models, metrics, and dashboards need to be built first, query-only MCP implementations require humans to do that work manually. Platforms that support both building and querying allow AI to participate in the full analytics lifecycle.

Vendor Capability Comparison

The table below compares MCP capabilities across major analytics platforms as of January 2026.

Capability	GoodData	ThoughtSpot	Tableau	Power BI	Omni
Modeling (generate/modify semantic model)	Semantic layer access; create and modify logical data models, metrics, attributes, and facts	Reads existing semantic layer but cannot modify model structure	Read-only access to published data sources	(Modeling MCP): CRUD operations on tables, columns, measures, and relationships via TMDL/TOM	Reads the semantic model but cannot edit
Metrics Development	Create and edit metrics (MAQL-based), context-free reusable metrics	Uses existing metrics only	Consumes existing metrics	Create and modify DAX measures programmatically	Uses existing metrics
Access Catalog / Content Discovery	Browse analytics catalog including semantic objects, dashboards, and visualizations with search and filtering	Browse Liveboards, structured data, and Spotter-generated content	List workbooks, views, and data sources with metadata access	Partial: Modeling MCP supports model exploration; Remote MCP enables schema retrieval	Query-oriented access to models and datasets with metadata
Query Data	Natural language to SQL/MAQL execution with governance and row-level security	Natural language queries via Spotter, interactive Liveboards	Query published data sources via VizQL with NL translation	(Remote MCP): DAX query generation and execution, Copilot-enabled	Core capability: NL queries translated to SQL
Execute Code / Analysis	Supports agent-driven analysis workflows, including visualization generation	Partial: Spotter performs autonomous analysis with follow-up questions	Limited to data retrieval and admin tasks	Partial: Can generate and validate DAX but not full workflows	Partial: Multi-step iterative analysis supported
Create Alerts / Automations	Create alerts with conditions, schedules, webhooks, and notifications	Not exposed via MCP	No MCP automation tools	Outside MCP scope	Not documented
Chat with Data	Conversational exploration; MCP enables external AI clients to query	Conversational AI with semantic context via MCP	Natural language interface to Tableau data	(Remote MCP): Schema-aware NL queries via Copilot and agents	Primary use case

Understanding the Differences

Most analytics platforms support querying through MCP. This reflects a shared focus on making existing analytics more accessible through conversational interfaces and natural language queries.

Support for building and modifying analytics infrastructure is less common. Many platforms were designed for human interaction through user interfaces, not programmatic modification of semantic layers. As a result, MCP in these systems is limited to read operations; AI can ask questions but cannot change the underlying analytical structures.

For teams evaluating MCP platforms, the key question is not which vendor supports MCP, but what your AI systems will actually need to do. If you have mature analytics that need to be more accessible, query-focused platforms work well. If you're building new analytics infrastructure or need to maintain and evolve it continuously, you'll need platforms that expose write operations through MCP.

Practical Implications

Organizations with mature semantic layers and well-maintained metrics often benefit immediately from query-focused MCP. Business users get faster access to insights without waiting for analyst support, and exploratory analysis becomes more efficient.

Organizations building new analytics or maintaining existing ones as business logic evolves face a different challenge**. Query-focused MCP doesn't reduce the work** required to build semantic models, define metrics, create dashboards, or set up recurring reports. That work still requires manual effort unless the platform exposes those capabilities through MCP.

Consider a company deploying customer-specific analytics to 100 enterprise clients. Each needs regional metrics, localized dashboards, and automated reports. With query-only MCP, AI can answer questions for each customer, but humans must manually configure 100 separate analytics environments. With platforms that support building through MCP, AI can generate workspace configurations, deploy dashboards, and set up automations programmatically — with human oversight at key decision points.

The difference is not just speed. It's whether analytics work scales with team capacity or with automation.

Questions to Ask During Evaluation

When evaluating MCP-powered analytics platforms, these questions help clarify what's actually possible:

Can my MCP server create a new metric, or only query existing ones?
Is my AI agent modifying my semantic model in real-time, or is it just querying it?
If I need to deploy 50 identical analytics environments with regional variations, can MCP automate that deployment, or is it a manual process?
What percentage of my MCP tools provide write access versus read-only access?
Can AI create alerts and schedule automations through MCP, or only generate insights?
If our business logic changes monthly and our semantic layer must adapt, can AI help maintain those definitions, or do we still need dedicated BI resources for every change?

The answers reveal whether a platform treats MCP as a query interface or as access to the full analytics lifecycle, and whether it can reduce the operational work of maintaining analytics or only improve access to existing results.

Next Steps

MCP adoption in analytics is still relatively new, and vendor capabilities continue to evolve. Rather than attempting full-scale adoption, most organizations benefit from a focused evaluation: identify one analytics workflow that currently requires significant manual effort, and test whether MCP can meaningfully reduce that work.

This approach reveals more about real-world fit than feature demonstrations or vendor materials. It also clarifies whether the platform's MCP implementation aligns with where your team actually spends time and what would most improve your analytics operations.

Understanding these capability differences helps organizations choose platforms that match their actual needs, whether that's making existing analytics more accessible, reducing the work required to build new analytics, or both.

Document Version: 1.0 | January 2026

Analysis based on: Public vendor documentation, MCP server specifications, and publicly available implementation details as of January 2026.

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