"TOML" matching MCP servers:

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 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.

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

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.

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.

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.

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.

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

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.

A Windows-compatible Model Context Protocol server that enables AI assistants to interact with Google's Gemini CLI, supporting file analysis, large context windows, and safe code execution.

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 LLMs to safely execute pandas code for data analysis and generate interactive visualizations through MCP protocol.

Enables interactive MATLAB development by allowing users to execute scripts and specific code sections, manage workspace variables, and capture generated plots. It integrates with the MATLAB Python Engine to provide persistent execution context for MCP-compatible clients.

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

A Docker-based FastMCP server for dynamic Python code execution with support for multiple MCP clients, automatic validation, runtime installation, and browser automation.