"Desktop Operations" matching MCP servers:

Exposes the full capabilities of Microsoft Dynamics 365 Finance & Operations to AI assistants through 49 comprehensive tools and standardized protocol interactions. It enables sophisticated workflows including OData operations, metadata discovery, and secure database analysis.

Provides structured MySQL database operations for Claude Desktop, converting natural language requests into safe, parameterized queries.

Enables AI assistants to explore metadata, query data, and perform write operations across multiple Microsoft Dynamics 365 Finance & Operations environments. It features specialized tools for OData execution and data analysis with built-in read-only safety for production environments.

Enables database inspection, backup/restore, analysis, and querying, plus media library and Windows Registry management via MCP tools.

Model Context Protocol server that enables querying and modifying live Power BI Desktop models by connecting to the in-memory Analysis Services engine.

Enables Claude Desktop to connect to PostgreSQL databases, allowing users to query, explore schemas, and analyze data using plain English without writing SQL.

Connects Claude Desktop to a local LiteFarm installation, enabling farm management, task operations, crop browsing, and direct SQL database operations through natural language commands.

Enables querying and analysis of container terminal operations including vessel tracking, crane management, and productivity metrics through natural language interactions with a MySQL database.

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

Enables Claude Desktop to interact with PostgreSQL databases through natural language for schema exploration, data analysis, and query execution. Users can search schemas, describe tables, and perform read or write operations without needing to write manual SQL.

Enables natural language querying of SQL databases by allowing the agent to explore schema, write SQL, and self-correct errors. It integrates with MCP-compatible assistants like Claude Desktop or Cursor.

Enables secure interaction with MySQL databases through MCP, offering tools for SQL queries, schema inspection, and data sampling, with support for multiple databases, SSH tunnels, and safe write operations.

A cooperative guardrail for AI agents that blocks destructive shell commands, SQL statements, or cloud operations until a human approves them out-of-band, with a tamper-evident local ledger.

Enables read-only SQL queries and schema inspection for legacy MySQL 5.0–5.6 deployments using SHOW-based metadata tools, without write operations or modern authentication support.

Enables MCP clients to interact with a 达梦 DM8 database over stdio, providing read-only queries, schema exploration, and optional gated write/DDL operations with strict safety checks.

Diagnoses and manages PostgreSQL logical replication including slot WAL retention, walsender lag, stuck subscriptions, and provides gated remediation tools for safe AI-driven operations.

DBeast MCP is a robust, security-first Model Context Protocol server designed to empower AI coding assistants (like Claude Desktop) with deep PostgreSQL database insights. Instead of just running basic SQL queries, DBeast equips your AI agent with tools to inspect active locks, audit slow queries, analyze table schemas, and safely generate index recommendations in real-time. Built specifically for

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.

Enables read-only interaction with an online store's SQLite database over MCP stdio, including table listing, schema inspection, safe read-only SQL execution, and sales analytics. It rejects mutating SQL operations to keep data intact.

MCP server for Seedfast, which fills PostgreSQL databases with realistic, relationally valid test data generated from live schema, providing tools for planning, running, and inspecting seeding operations.