"The search query 'test123'" matching MCP servers:

An advanced MCP server that provides an agentic interface to Google's Gemini 3.1 models via Vertex AI, combining real-time Google Search, URL analysis, and Python code execution to solve complex multi-step research and data tasks.

A Python MCP server that allows programmatic interaction to find torrents programmatically on ThePirateBay, Nyaa and YggTorrent.

The Fibery MCP server provides integration between Fibery and any LLM provider supporting the MCP protocol (e.g., Claude for Desktop), allowing you to explore Fibery Workspace Schema, query databases, create and update entities using natural language.

Enables integration of Perplexity's AI API with LLMs, delivering advanced chat completion by utilizing specialized prompt templates for tasks like technical documentation, code review, and API documentation.

A Python MCP server that allows programmatic interaction to find torrents programmatically on YggTorrent and La Cale.

A simple demonstration server for the MCP Python SDK that provides weather alerts for locations, allowing users to query weather information through Claude Desktop or Cursor.

A Model Context Protocol server that gives LLMs the ability to interact with Ethereum networks, manage wallets, query blockchain data, and execute smart contract operations through a standardized interface.

Enables AI agents to interact with the Roark API by writing and executing TypeScript SDK code in an isolated sandbox, with built-in documentation search to guide those tasks.

Enables AI agents to interact with the FunctionFly platform for registry search, function execution, publishing, analytics, vault secrets, and stateful workflows.

MCP server for exploring the ECMAScript specification and its implementation, enabling semantic search of spec sections, JavaScript execution in engine262, and access to a knowledge graph linking spec sections to implementation functions.

Enables interaction with the complete Cloudflare API using a codemode pattern that allows agents to search the OpenAPI spec and execute API calls via JavaScript. This approach minimizes token usage by keeping the massive specification on the server and only returning relevant results.

Enables an LLM to analyze documents far larger than its context window by issuing symbolic search, filter, aggregation, and recursive sub-query commands that execute server-side, returning compact handle stubs instead of full result sets. It also lets agents stash context server-side as expandable memos, cutting token usage across long sessions.

Enables AI assistants to search manga/anime, write and run code, get weather, save notes, and query JoJo Stands data with real data from public APIs. Integrates with Claude Code, Cursor, and other MCP-compatible clients.

A server that provides access to the tidymodels R ecosystem, enabling users to list packages, search functions, generate code templates, and search issues through AI assistants like Cline.bot and Claude Desktop.

Enables executing Python scripts, rendering scenes, and controlling 3D objects in Blender via Claude, with support for Cycles and EEVEE engines.

Enables AI assistants to query YashanDB using natural language through SQL operations.

An experimental server implementing the Model Context Protocol to allow AI assistants to query and analyze financial data stored in Beancount ledger files using the Beancount Query Language.

Enables AI agents to directly manipulate Jupyter Notebook cells and structure through a secure, well-defined API, overcoming Cursor's AI agent mode limitations.

Lets an agent query Linear by writing JavaScript that the server runs in a throwaway worker, exposing schema introspection and a single request helper so only the return value comes back. Runs read-only by default, refusing mutations before any request leaves the process unless explicitly enabled.

A Python wrapper that makes Xcode's MCP bridge compatible with strict spec-compliant clients like Cursor by fixing response formatting. It enables AI assistants to build projects, run tests, and query Xcode project data through the Model Context Protocol.