"Understanding Inference Models" matching MCP servers:

Provides deterministic tools for understanding, transforming, and verifying structured data via MCP, enabling rule inference from examples and verification of transformed records.

An open-source MCP server that connects to various data sources (SQL databases, CSV, Parquet files), allowing AI models to execute SQL queries and generate data visualizations for analytics and business intelligence.

An MCP server that allows AI models to create data visualizations using Vega-Lite syntax by providing tools to save data tables and generate visualizations from them.

Enables incremental model scoring over an append-only event log, with a tamper-evident, hash-chained audit trail so every decision can be reviewed and independently re-verified.

Enables local and offline XLSX header cataloging, candidate relationship inference, and Oracle SELECT/WITH SQL generation without accessing databases or executing SQL.

An MCP server that exposes VeloDB analytics data to AI clients through a governed semantic metrics layer (MetricFlow) and raw SQL fallback, with multi-workspace isolation, staging workflows, and a web UI for managing models.

Connects MCP clients to databases like Snowflake, BigQuery, and Databricks through Alkemi's data platform, enabling natural language database queries with proper schema understanding and metadata management for consistent team-wide access.

Enables DeepSeek Harness models to directly call local ArcGIS Pro via MCP tools for reading projects, layers, GDB structures, and executing Buffer/Clip/custom ArcPy operations locally.

A production-ready MCP server that provides comprehensive dbt project quality assessment for any GitHub repository, enabling AI agents to analyze dbt models, check metadata coverage, and map data lineage.

MCP server for the OrionBelt Semantic Layer. Enables LLMs to explore governed semantic models, compile YAML metric definitions into optimized SQL across 8 engines (BigQuery, ClickHouse, Databricks, Dremio, DuckDB, MySQL, PostgreSQL, Snowflake), and execute analytics queries via natural language. Works with Claude, Cursor, Windsurf, and Copilot.

Enables querying open Power BI Desktop models locally, including discovering instances, listing tables and measures, and running DAX queries without cloud login or publishing.

Provides read-only access to MetaTrader 5 market data, trading history, and technical analysis through Python execution. Supports querying price data, calculating indicators, creating charts, and generating forecasts with Prophet and ML models.

MCP server for connecting to Microsoft Fabric and Power BI semantic models, enabling workspace browsing, dataset management, TMSL retrieval, DAX queries, model creation/editing, and includes a Best Practice Analyzer with 71 rules.

Enables language models to run data-quality checks and profiling on local files, using dbt-style assertions like not_null, unique, relationships, and accepted_values.

Turns Google Sheets, CSV, Excel, and Parquet files into durable, queryable data for AI agents. Lets clients securely discover datasets, inspect semantic models, run bounded analytics, and manage approved semantic overlays.

A Model Calling Protocol server that enables LLMs to perform rigorous Bayesian analysis and probabilistic reasoning, including inference, model comparison, and predictive modeling with uncertainty quantification.

Enables LLM clients to analyze and interact with Power BI (.pbix) files, providing access to data models, DAX measures, Power Query code, table relationships, and data contents through natural language.

A unified backend MCP server that aggregates and filters data from external services like Zaim, GitHub, Google, and Notion, providing cross-cutting views via MCP and REST API without heavy AI inference.

Enables agents to manage Microsoft Fabric workspaces, items, lakehouses, warehouses, and notebooks via the control plane, and run guarded read-only SQL plus optional DAX queries against semantic models.

Enables authorized AI agents to inspect, query, create, modify, validate, refresh, and manage eligible cloud semantic models in Microsoft Fabric and Power BI through stateless Streamable HTTP.