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    Human-as-a-Service for AI agents. When your agent is blocked by a task that requires a real human — accepting ToS, creating accounts, submitting forms, identity verification — it calls NeedHuman. A human completes the task and returns the result with proof.
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    MIT
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    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
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    Apache 2.0
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    It gives AI agents one tool to access the Omega-API judge, which evaluates conversational turns and returns a decision and gate action (release, clarify, suppress, or none) to guide the agent's response.
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    Apache 2.0
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    Connects MCP clients directly to a live Godot editor, enabling AI assistants to build scenes, edit nodes, and control the editor through over 120 operations.
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    MIT
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    An MCP server that enables local AI models to receive guidance from remote 'senior' AI providers like OpenAI, Anthropic, and Gemini to solve programming problems. It features intelligent multi-turn dialogue management, context synchronization, and automated session history tracking.
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    Provides an interactive checklist tool that allows AI agents to present step-by-step instructions to users through an automatically opened terminal UI. It enables agents to guide users through manual tasks and wait for completion, skipping, or feedback before proceeding.
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    An adaptation of the MCP Sequential Thinking Server designed to guide tool usage in problem-solving. This server helps break down complex problems into manageable steps and provides recommendations for which MCP tools would be most effective at each stage.
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    MIT
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    An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.
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    One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose
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    MIT
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    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.
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    Manage and monitor Hadoop clusters via Apache Ambari API, enabling service operations, configuration changes, status checks, and request tracking through a unified MCP interface for simplified administration. * Guide: https://call518.medium.com/llm-based-ambari-control-via-mcp-8668a2b5ffb9
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    MIT
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    An MCP server that provides a multidimensional philosophical framework for prompt engineering by mapping intent across ten distinct branches of thought. It generates a principled construction basis, including active constructs and spectrum tensions, to guide the creation of structured and intentional prompts.
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    MIT