"How to access and read a local SQL database" matching MCP servers:

Enables Cursor AI to interact with SQL Server databases, including querying, schema exploration, report generation, and chart creation.

Enables answering business questions over local SQL Server WideWorldImporters data through MCP, with support for both STDIO and Streamable HTTP endpoints.

Enables discovery of Fabric/Power BI workspaces and semantic models, execution of DAX queries, and generation of executive HTML reports with visualizations.

Local-first MCP server for Power BI Desktop automation. Automate semantic model changes, DAX, Power Query, Excel, and report layout from MCP-capable AI clients.

Enables an agent to turn a paper's draft into editable Figma frames for publication-ready figures, checking every number against the paper and verifying the exported PDF.

Enables AI assistants to programmatically read, modify, and query Tableau workbook files (.twb and .twbx) on the local filesystem via 80 tools.

A secure multi-database MCP server supporting MySQL, PostgreSQL, and SQLite with read-only enforcement, SQL injection prevention, and tools for schema analysis, performance optimization, and visualization.

A Model Context Protocol (MCP) server for Microsoft SQL Server that provides tools for database operations, data analysis, and visualization generation.

Enables users to inspect local Hyper files by listing files, schemas, and tables, previewing up to 100 rows, and generating sample sales Hyper data.

Enables rendering interactive UI widgets and data visualizations for Claude.ai through MCP, supporting callback-based forms and client-side charts.

Enables users to create, validate, view, and export system stock-and-flow maps from a versioned JSON model through MCP-capable hosts over local stdio or hosted Streamable HTTP. Supports causal-loop and nested-level mapping while keeping submitted model content with the caller and requiring no LLM provider key.

Enables generating Power BI .pbit dashboards from CSV files or live SQL databases using plain-English prompts and optional reference-image layouts, with local-only processing and MCP tools for Claude Desktop.

Enables AI clients to inspect and modify maps and scenes open in a locally running desktop GIS session through natural language, exposing 38 fixed tools for session and layer queries, fields, selection, visibility and renaming, symbology, transparency, camera and elevation, map switching, raster, layout export, whitelisted geoprocessing, and attribute editing. It routes stdio MCP calls through a Windows named pipe to a C# add-in, with no network ports and no arbitrary code execution.

An HTTP MCP server for exploring CSV files that masks PII before any code execution, enabling safe data analysis with local or cloud LLMs.

Enables Codex to control a Gephi 3D network view through natural language, including network generation, 3D layout, node focus and neighbor highlighting, layered views, and PNG/PDF export.

A local, fully vectorized computational engine for stock market analysis that enables AI to perform factor calculation, strategy backtesting, IC analysis, and GPU-based multi-dimensional visualization using local DuckDB data.

Enables Claude to interact with local workspace files through MCP, including listing files, summarizing CSV datasets, generating plots, executing Python scripts, and running allowlisted shell commands from a chat interface.

Enables AI assistants to discover and interact with local Power BI Desktop instances, including inspecting table/column schema and executing DAX queries.

A read-only MCP server that connects Claude to the Northwind sample PostgreSQL database, allowing natural language queries to generate SQL and charts.

Enables conversational spatial SQL queries in plain English, turning natural language questions into validated PostGIS operations and rendering results as GeoJSON on an interactive map.

Enables AI assistants to connect to StarRocks databases for direct SQL execution, schema and database exploration, system metrics access, and Plotly chart generation. It also provides cached table and database overviews.

Enables AI assistants to connect to StarRocks databases for direct SQL execution, schema and metadata exploration, table/database overviews, system metrics access, and Plotly chart generation from query results.

Bridges AI assistants with StarRocks databases, enabling direct SQL query and DDL/DML execution, database and table schema exploration, system metric access, and query-result visualization as Plotly charts.

Options analytics an agent can drive: import a permitted chain, solve implied volatility, build a sixteen-Greek ladder, map dealer gamma walls, rank twenty-three structures, run a GARCH-t simulation with a convergence gate, and backtest with modelled premiums. Twelve stdio tools; every output is a schema-validated JSON file with source, assumptions and a degraded flag. Research only, no orders.

It bridges AI assistants and StarRocks databases, enabling direct SQL execution, database/schema exploration, system metric access, detailed table/database overviews, and Plotly chart generation without complex client-side setup.

Enables AI assistants to connect to StarRocks databases for direct SQL execution, database and schema exploration, detailed table/database overviews, and Plotly chart visualization of query results.

It enables AI assistants to execute SQL, explore databases and schemas, access system metrics, retrieve table and database overviews, and generate Plotly charts from query results.

Renders word-sized charts — sparklines, bars, bullets and 100+ more types — to self-contained SVG with generated alt text, small enough to sit inline in a model's reply or a table cell. Three tools: find the right chart for a question, get its wiring, and render it.

A Model Context Protocol server that gives an LLM a structured visual layer to describe what exists and get back a clean, precise SVG diagram instead of ASCII art.

A unified Model Context Protocol server for data engineering and analytics that lets LLM clients query, profile, transform, and visualize data using DuckDB and PySpark, while keeping raw data out of the model's context through read-only, bounded, and audited operations.