"Optimizing Meta ads campaigns and TikTok marketing" matching MCP servers:

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.

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

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.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

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

A production-ready foundation template for building Model Context Protocol (MCP) servers with FastAPI, featuring modular tools, comprehensive testing, and OpenShift deployment configurations. Includes automated transformation scripts to create custom domain-specific MCP servers.

Connects any MCP client to TikTok Effect House, letting AI agents build and edit effects live by conversation — running JavaScript cells against the editor's scene, script, asset and preview APIs, with docs search and editor launching. It also exposes the editor's automation tools for scene editing and preview captures, without needing the in-app AI panel.

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

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.

Extracts minimal, relevant code context from multiple programming languages while analyzing diffs and optimizing imports to reduce token usage for AI assistants. Supports TypeScript/JavaScript, Python, Go, and Rust with token-aware caching.

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.

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.

Routes code generation tasks between local models and Claude Cloud, optimizing cost by handling simple tasks locally and reserving cloud thinking for complex tasks.

Transforms Claude from a code generator into a programming partner capable of testing, debugging, and optimizing code automatically through a secure execution environment.

Hardened MCP bridge to the full Ollama ecosystem — local and cloud models. 6 tools covering health, model management, chat, and generation with SSRF mitigation, singleton HTTP client, and structured error handling. Two deps, one file.

Composes arbitrary MCP tools using JavaScript, enabling discovery, composition, and execution of other MCP servers through a single 'exec' tool.

A persistent Python REPL MCP server for AI agents with stateful execution, real timeouts, crash isolation, and an MCP bridge to call other tools in the project.

MCP server that gives AI assistants a secure gVisor-isolated sandbox to run Python or JavaScript code, returning stdout, stderr, and exit codes without local access.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

Enables running one Codex CLI task at a time in an isolated temporary folder, returning Codex's answer and a manifest of produced files for manual review and selective copying.

A code-execution MCP server for Klaviyo and Shopify analytics, enabling safe JavaScript execution in a V8 sandbox with state persistence across calls.

Enables AI assistants to run code in 24 programming languages on HackerEarth's sandboxed servers and read the printed output or errors, without installing local toolchains. It also lists supported languages with exact identifiers for use when submitting code.

Enables MCP-compatible AI assistants to run Python code on Google Colab GPU/TPU runtimes, supporting accelerators like T4, A100, H100, with background execution and Google Drive integration.

An MCP bridge that lets Codex supervise Reasonix as an implementation worker in an isolated Git worktree, ensuring safe delegation with scoped writes and sandboxed execution.

Local MCP server that registers restricted Python filters and runs them against local JSON, YAML, and TXT files, enabling safe data filtering and file conversion.

Enables AI agents to interact with Jupyter notebooks via MCP tools for querying, modifying, executing, and setting up notebooks, with state preservation and real-time collaboration.