"Semantic layer for SQL query execution with dimensions and measures" matching MCP servers:

Enables AI agents to interact with the Execute.run bot API for managing Shell balances, transferring funds, and executing LLM requests. It provides tools for identity verification, transaction tracking, and performing compute tasks through the Execute.run platform.

An MCP server that connects LLMs to SQL databases for development assistance, enabling query execution, schema exploration, and data manipulation while providing safety controls against destructive operations.

An MCP server that enables AI agents to execute terminal commands and Python code on the host system, leveraging Agent Zero's battle-tested implementation with session management and smart output handling.

Enables LLM agents to explore and query a Postgres database through secure, read-only tools.

A runtime for inspectable agent workflows that provides MCP tools, bounded Python execution, session memory, and deterministic evaluation.

A queue-based execution server that monitors a command queue and executes shell commands or Python scripts, moving completed or failed jobs to respective directories.

Enables efficient AI agent operations through sandboxed Python code execution with progressive tool discovery, PII tokenization, and skills persistence, achieving up to 98.7% token reduction by processing data in a sandbox rather than in context.

Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.

An MCP server that lets Claude query a mock business SQL database in plain language through read-only tools, with server-side guardrails that enforce SELECT-only queries and block access to sensitive payment data.

semantic-edit-mcp

A FastMCP server that enables natural language querying of PostgreSQL databases through LLM integration, allowing users to generate SQL queries from plain English and visualize the results.

Enables agents to run SQL queries against PostgreSQL databases through MCP with connection pooling, tenant isolation, and read-only guardrails. Supports introspection of schemas, tables, and columns.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

A minimal MCP server that exposes a run_command tool over stdio, enabling command execution within a Docker container.

Spawns multiple candidate reasoning paths concurrently, scores their progress against milestones, and halts underperforming branches early to cut inference costs.

Enables execution of code in a sandbox environment with integrated web search capabilities. Provides a basic framework for running code safely, primarily designed for AI agents and research applications.

Enables AI agents to execute C++ code, test solutions against multiple test cases, analyze performance, and generate test cases using OnlineGDB's online compiler. Perfect for solving competitive programming problems with iterative self-correction capabilities.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

Provides a sandboxed Docker environment for executing Python code against API endpoints, exposing tools for discovery, inspection, execution, and reuse.

Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.