"Meta" matching MCP servers:

Figma MCP server that lets agents execute arbitrary JavaScript inside a running Figma desktop app via a local development plugin, enabling real-time document inspection and manipulation while avoiding REST API rate limits.

Lightweight MCP server for After Effects that enables Cursor Agent to execute ExtendScript code inside AE via a single tool, similar to Blender MCP.

Enables AI models to dynamically create and execute their own custom tools through a meta-function architecture, supporting JavaScript, Python, and Shell runtimes with sandboxed security and human approval flows.

Aggregates tools from multiple MCP servers, generates TypeScript definitions, and executes custom TypeScript scripts to orchestrate cross-server tool calls.

Lets AI assistants use MetaModel's formula engine for certified computation, turning published calculators, pricing tools, and engineering models into AI-callable tools with no hallucinated math.

An MCP server that enhances sequential thinking with meta-cognitive capabilities including confidence tracking, hypothesis testing, and organized memory storage through graph-based libraries and structured JSON documents.

A meta-MCP server that helps users create new MCP servers through AI guidance, templates, and streamlined workflows, transforming ideas into production-ready implementations with minimal effort.

A proxy server that unifies multiple MCP servers, enabling seamless tool, prompt, and resource management via the MetaMCP App.

Aggregates tools from multiple upstream MCP servers and exposes them through 4 meta-tools, enabling LLMs to discover and use hundreds of tools without loading all schemas upfront.

Enables agents to use many MCP servers without context bloat by exposing meta-tools (search, load, call, run_code) that reduce token usage via progressive disclosure and result trimming.

A self-improving coding agent MCP server that enables code execution, semantic memory, and reusable skills via sub-agents, turning your AI client into a meta-operating system.

A meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.

This MCP server wraps MetaCall FaaS API endpoints as tools, enabling LLMs to list deployments, call functions, and deploy repositories directly via natural language.

A meta-MCP server that orchestrates tools from multiple MCP servers, enabling complex Python workflows with loops and conditionals.

Enables AI assistants to interact with the MetaTrader 5 trading platform for market data analysis, placing trades, and managing trading positions. Provides comprehensive access to forex and financial market operations through the Model Context Protocol.

Context-Pods is a comprehensive development framework for creating, testing, and managing Model Context Protocol (MCP) servers. It provides a Meta-MCP Server that can generate other MCP servers through natural language descriptions or by wrapping existing scripts.

Enables interaction with the Metal Framework by providing documentation search and code generation capabilities using natural language queries.

Exposes MetaCall FaaS capabilities as MCP tools for deploying, invoking, and managing serverless functions.

A unified MCP server providing tools for web search, GitHub analysis, document parsing, browser automation, secure code execution, and forum parsing, enabling LLMs to perform a wide range of external tasks through a single backend.