"A server for viewing dev tool console information on MCP" matching MCP servers:

Turns any OpenAPI spec into a single-tool MCP server where the model writes JavaScript against a typed api object in a QuickJS sandbox, so multi-step tasks run in one round trip and only the final result reaches context. Credentials, rate limiting, retries, write gating and audit logging stay on the host, outside the sandbox.

Enables coding agents to treat a remote Linux host over SSH as a local working tree, exposing editor-semantics operations, background job management, artifact transfer, and guard hooks through MCP.

Facilitates interaction with AWS Bedrock-enabled tools by implementing Anthropic's Model Context Protocol for seamless tool integration and communication.

Enables AI assistants to execute Ruby console commands (Rails console, IRB, etc.) with persistent session support for interacting with Ruby/Rails applications through natural language.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

Agent development tool-kit

An MCP server that integrates Codex CLI into Claude Code workflows for code writing, execution, and review with session management. It features real-time progress monitoring via a local HTTP dashboard and supports detailed configuration for various coding tools.

A Model Context Protocol server that provides Claude with a dedicated space for structured thinking during complex problem-solving tasks, helping improve its reasoning capabilities.

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.

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

An MCP server that provides tools for frontend development, including i18n issue detection, OpenAPI type generation, and project structure analysis.

MCP server connecting Claude/Cursor to Codex CLI, enabling code analysis via @ file references, multi-turn conversations, sandboxed edits, and structured change mode.

Enables AI assistants to leverage Qwen's code analysis capabilities with large context windows, supporting file/directory analysis, sandbox execution, and multiple approval modes for safe code operations.

Create sandboxed local development environments directly from Github and execute tests, see coverage and more! Supports Python, Node, Bun and many test runners.

Enables AI clients to inspect and control a live QGIS instance to verify plugin behavior, including observing UI state, triggering actions, running algorithms, and executing session-permitted code.

MCP server that integrates Google Gemini CLI with AI assistants, enabling large file analysis and natural language queries without an API key.

A Model Context Protocol server that enables AI assistants to interact with Google Gemini CLI, allowing them to leverage Gemini's large token window for analyzing files and codebases using natural language commands.

Enables on-demand generation of TypeScript tools using an LLM with human-in-the-loop approval and safe sandboxed execution. It allows users to create and persist custom functionality through natural language requests.

Enables AI assistants to interact with multiple AI models through the OpenCode CLI with a unified interface. Supports plan mode for structured analysis, flexible model selection, and natural language queries with file references.

Enables Windows-native developer orchestration for Claude Code and Codex with PowerShell discovery, capability routing, safety-gated execution, package management, and sandbox-based isolation.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

This is a Model Context Protocol (MCP) server that allows AI assistants to interact with the OpenCode CLI. It enables AI assistants to leverage multiple AI models through a unified interface, with features like plan mode for structured thinking and extensive model selection.

An MCP server that enables AI assistants to build, test, and debug Zotero 7/8 plugins through tools for UI inspection, JavaScript execution, and real-time log monitoring. It integrates with Zotero's Remote Debugging Protocol to provide rich context including screenshots, DOM access, and database querying.

Go Development MCP Server for Claude Desktop and other AI tools

A FastMCP server that dynamically loads tools from Python/YAML file pairs, acting as a stand-in for the Tools Framework in agentic-assembly-framework.

Provides AI agents with shell execution and file management capabilities on a development VM, including running commands and editing files via tools like run_command, read_file, and edit_file.

A deployable MCP server for Cloudflare Workers or Azure Functions that provides example tools (time, echo, math), prompt templates for code assistance, and configuration resources. Enables AI assistants to interact with edge-deployed services through the Model Context Protocol.

An MCP Server that enables users to interact with Google's On-Demand Scanning API (v1beta1) through natural language, likely providing capabilities for vulnerability scanning and security analysis of containers and artifacts.

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.

Enables agents to synthesize typed MCP tool schemas at runtime, validate Python ASTs for unsafe imports, compile sandboxed callable handlers, and hot-plug them into an active MCP runtime. It provides a zero-dependency, standard-library-only MCP server for safe just-in-time tool creation and execution.

Enables agents to dynamically generate typed MCP tool schemas, validate Python execution ASTs against unsafe imports, and hot-plug sandboxed callable handlers into an active runtime. This allows new tools to be synthesized and safely executed on demand.

An MCP server that allows Claude Code to interact with the OpenAI Codex CLI.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

A Model Context Protocol implementation that plays sound effects (completion, error, notification) for Cursor AI and other MCP-compatible environments, providing audio feedback for a more interactive coding experience.

Enables agents to accomplish multi-step work by writing a single sandboxed JavaScript program that drives shell, filesystem, HTTP, and every tool of any configured MCP server, returning only a small summary instead of raw JSON. This collapses dozens of advertised tool schemas into one tool, cutting per-request context by roughly 26× while keeping disabled upstream servers reachable.

Bridges Claude Desktop to local Windows 10 development environments to enable repository reading, documentation searching, and isolated code execution. It provides a secure interface for running allowlisted terminal commands and scripts directly through natural language.

An MCP server that grants AI agents unrestricted file system, Python, and PowerShell access on Windows for real, unfiltered automation.

A lightweight server that provides curated, high-quality prompts for common development tasks like UI/UX design, project setup, and debugging to enhance AI-powered development workflows.

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

Enables GUI automation by executing Python code with instrumented pyautogui, providing MCP-friendly output and inline screenshot delivery.