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"AI Footprints - Environmental and Digital Impact of Artificial Intelligence" matching MCP servers:

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    Enables detection of AI-generated content from ChatGPT, GPT-4, and Gemini models through the BACH AI Content Detector API.
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    MIT
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    This MCP server provides AI-native revenue intelligence tools for B2B teams, enabling signal detection, constraint analysis, pipeline health scoring, and GTM change proposals via HubSpot integration.
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    Business Source 1.1
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    An MCP server that provides AI agents with ICP Triangulation Framework™ for scoring prospects across firmographics, behaviors, and growth signals, plus RFM analysis and pipeline health scoring, with optional HubSpot integration.
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    Business Source 1.1
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    Aggregates real-time threat intelligence from multiple sources including Feodo Tracker, URLhaus, CISA KEV, and ThreatFox, with IP/hash reputation checking via VirusTotal, AbuseIPDB, and Shodan for comprehensive security monitoring.
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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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    Enables AI assistants to query KumoRFM for predictive analytics on relational data, including graph management, natural language to PQL conversion, and training-free predictions.
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    MIT
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    Enables bidirectional communication between MCP clients and Slack, allowing users to receive task notifications and respond to AI inquiries directly within Slack threads. It supports various urgency levels, message threading, and interactive question-and-answer workflows.
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    MIT
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    Enables comprehensive threat analysis for Indicators of Compromise (IoCs) including IP addresses, file hashes, domains, and URLs. It provides detailed reputation scores, security vendor evaluations, and network metadata to facilitate security assessments and risk detection.
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    MIT
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    PhysBound is a specialized "Physics Linter" for AI that deterministically validates RF and thermodynamic claims against hard physical limits, preventing hallucinations in engineering workflows.
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    MIT
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    Enables AI assistants to search and filter Azure platform updates, including previews, general availability, and retirements, through natural language queries.
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    MIT
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    Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.
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    Apache 2.0
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    Enables AI assistants to interact with Intervals.icu fitness tracking and wellness data, allowing users to fetch, filter, and group activities or health metrics. It provides structured summaries of workouts and physical well-being through natural language queries.
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    Enables management of Kylas CRM lead operations, including creating leads, searching and filtering records, and resolving user, product, or pipeline IDs. It provides specialized tools for monitoring idle leads and accessing lead schema instructions through natural language.
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    MIT
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    Provides policy-based access control, incident tracking, and compliance monitoring to govern AI agent behavior. It enables organizations to enforce security rules and maintain audit trails by validating agent actions against trust levels and pattern-based policies.
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