A Model Context Protocol server that retrieves detailed geographic information about IP addresses using the ipinfo.io API, helping determine user location and network details.
An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone.
Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.
Search and retrieve detailed information about Swiss companies from the official Zefix register, including company profiles, corporate structures, and SHAB publications.
An MCP server that provides information about Utkarsh, including bio, skills, work experience, and portfolio projects, accessible via local stdio or remote HTTP with OAuth.
Provides access to Aceternity UI component library documentation, enabling AI assistants to browse, search, and retrieve detailed information about components.
Enables AI assistants to search and retrieve information about Terraform providers and modules from the public Terraform registry, including detailed documentation, version information, and resource specifications.
Provides real-time system metrics and information through a Model Context Protocol interface, enabling access to CPU usage, memory statistics, disk information, network status, and running processes.
Exposes information about Z-Image AI image generation and editing platform, including styles, pricing, and official links, for MCP-compatible AI clients.
Connects MCP-compatible coding agents to VMware vCenter to query information about virtual machines, hosts, clusters, datastores, datacenters, and networks.
An MCP server that provides detailed Pokémon information by integrating with the PokeAPI, allowing users to fetch comprehensive data about Pokémon and simulate battles.
Provides tools to access information about religious institutions and community services in Kenya, including Catholic dioceses, Protestant denominations, Muslim communities, and welfare programs.
Enables large language models to retrieve up-to-date and correct information about dlt pipelines, datasets, and schemas, improving AI-assisted development.
Enables users to ask natural-language questions about a local folder's contents, including file names, sizes, and timestamps, with rollups by type and age and cleanup suggestions for stale or large files. It returns metadata only and cannot read, move, or delete files.