"Information about theHarvester tool" 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.

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.

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.

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.

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

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

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 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.

Enables schema-validated, sandboxed access to 34 Python-powered tools across Pillow, requests, pypdf, pandas, openpyxl, and python-docx, plus an autonomous ReAct converter agent that can grow the catalog by requirement and self-repair at runtime. It also provides a control console and hardened Docker deployment for secure MCP client integration.

Enables secure execution of Python scripts in isolated Docker containers via MCP, with resource limits, package allowlisting, read-only filesystem, and artifact return.

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.

Enables an LLM to perform safe arithmetic calculations and web searches through a standardized MCP interface, returning results to generate informed responses.

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

An MCP server for Task Trellis that provides tools for AI coding agents to manage tasks, currently featuring a simple hello_world demonstration tool.

Enables execution of TypeScript code to call MCP tools instead of direct tool calls, reducing token usage by up to 98% while orchestrating complex multi-tool workflows through secure sandboxed code execution.

Agent development tool-kit

MCP server for Python build123d to help AIs develop and reason about 3D models and CAD

Guides problem-solving by breaking down complex problems into steps and recommending appropriate MCP tools for each stage, with confidence scores and rationales for tool suggestions.

Enables compiling Lean 4 proof snippets on the local machine against a Mathlib project and returns structured compiler feedback — status, one-line hints, and the compiler's real error, warning, and information messages — so a model can iterate on proofs instead of guessing. Compilation is serialized, timeouts kill stuck tactics, and rejected code is reported as a normal result rather than a tool failure.

A powerful AI-driven tool that helps developers create beautiful, modern UI components instantly through natural language descriptions.

A comprehensive learning project demonstrating MCP server implementation with tools for calculator operations, file management, and system information retrieval, including Google ADK integration patterns and best practices.