"Bitcoin SV" matching MCP servers:

Enables AI agents to access pay-per-call web unlocking, structured extraction, clean scraping, Python sandbox execution, persistent key-value memory, and clearinghouse task bounties, with settlement via Bitcoin Lightning L402 or Base/Solana USDC x402.

Persistent Linux environments for AI agents, with interactive terminals, input-wait event feedback, background tasks, file transfers, and HTTPS previews. Connect through a local stdio MCP server. Requires an agend account; bring your own agent and model.

A bridge that enables AI assistants to connect to VS Code's debugger, allowing them to interact with and control debugging sessions through websocket connections.

A lightweight MCP server that gives AI coding assistants full development capability on ServiceNow through the Table API, enabling CRUD and script execution without local installation.

An MCP (Multi-Agent Conversation Protocol) Server that enables interaction with AWS S3 Control API through natural language, auto-generated using AG2's MCP builder.

Connects MCP-capable agents to the DTU Computing Center cluster for submitting, monitoring, and chaining LSF jobs without shell access.

Enables coding agents to inspect and mutate code in zero-copy RAM shadow workspaces, explore competing fixes in parallel, and commit only syntax- and test-validated changes to disk through a compact MCP tool surface.

Enables AI assistants to inspect, refactor, lint, run, and version Power Query (M) queries in Excel workbooks and Power BI projects, with snapshot safety and live evaluation.

Gives AI agents a searchable catalog of over 15,000 documented tools, commands and functions across fourteen engineering and design applications, plus headless automation adapters that run the installed software in batch/script mode and return structured JSON. Also provides a timezone-aware time tool and an optional live documentation search, with local-only networking and an internet-tools kill switch by default.

Enables execution of Snowflake stored procedures through the MCP server by providing a framework to define, manage, and call procedures from specific schemas or individual procedures.

A Model Context Protocol server that enables LLMs to interact with VS Code's Simple Browser, allowing for browser control, JavaScript execution, console monitoring, and navigation through natural language.

Enables external agents to run, edit, create, and manage the Jupyter notebook the user is actively editing in VS Code, headlessly and without approval dialogs. Works with any MCP client and is Jupyter-optional for document operations.

The ultimate Gemini API interface for MCP hosts, intelligently selecting models for the task at hand—delivering optimal performance, minimal token cost, and seamless integration.

Enables AI assistants to interact with VS Code for language intelligence, debugging, and code execution.

A Model Context Protocol server that enables Claude and other LLMs to perform database operations and invoke Edge Functions within Supabase through natural language.

Enables sandboxed Python execution and read-only SQL queries against a service database through MCP.

Provides a comprehensive integration between LLMs and the Godot Engine, enabling AI assistants to intelligently manipulate project files, scripts, and the live editor. It supports advanced workflows including version-aware documentation querying, automated E2E game testing, and real-time visual context capture.

Execute Python code with persistent state across Claude conversations using a background Jupyter kernel. Supports creating notebooks, managing virtual environments, and maintaining variables/imports between code executions.

exposes a remote MCP endpoint so agents can: run strategy backtests by symbol/timeframe/date range, pass strategy inputs programmatically, receive structured backtest results (trades, win rate, profit, drawdown), keep long-running runs observable via progress notifications, support Binance Futures tickers only, enforce a maximum of 1440 candles per backtest, apply a rate limit of 3 backtests per

Enables AI coding agents to execute tools in a sandbox, retain searchable persistent memory, and reversibly compress long conversation histories through a unified MCP endpoint.