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    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.
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    Model Context Protocol (MCP) server for GitLab — exposes 1006 GitLab REST & GraphQL API operations as MCP tools (42 meta-tools / 57 enterprise), 24 resources, 38 prompts, and 17 completion types for AI assistants. Written in Go, single static binary, stdio and HTTP transport.
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    MIT
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    Enables AI-powered exploration and interaction with GitLab instances through comprehensive search, code browsing, and repository management. Supports both self-hosted and GitLab.com with flexible authentication for read and write operations.
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    Enables interacting with GitLab repositories, merge requests, and code through natural language using MCP. Supports authentication with personal access tokens or OAuth2, and provides tools for listing projects, reading repository code, and analyzing merge request lifetimes.
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    MIT
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    A Model Context Protocol server for GitLab that provides AI agents structured access to projects, repositories, issues, merge requests, pipelines, and releases, with read-only defaults and operational controls.
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    A comprehensive integration providing over 46 tools for managing GitLab projects, issues, merge requests, and CI/CD workflows through natural language. It supports repository operations like local cloning and batch actions across groups via stdio, SSE, or HTTP transports.
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    MIT
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    A TypeScript MCP server that provides integration with GitLab's REST API, allowing users to interact with GitLab projects, issues, merge requests, pipelines, and more through natural language.
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    A Model Context Protocol server that enables AI assistants to interact with self-hosted GitLab instances through 30 specialized tools. It supports managing projects, merge requests, CI/CD pipelines, and repository operations via the GitLab REST API v4.
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    MIT
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    Enables large language models to retrieve up-to-date and correct information about dlt pipelines, datasets, and schemas, improving AI-assisted development.
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    Apache 2.0
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    MCP server for system monitoring, enabling users to ask about CPU, memory, disk, network, and processes on their system.
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    MIT