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"How to query data from a Snowflake database" matching MCP servers:

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    Query Microsoft Patch Tuesday security updates from the official MSRC API — monthly rollups, CVE/KB lookups, supersedence chains, and urgency-ranked triage enriched with EPSS scores and the CISA KEV catalog. No API keys required.
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    4
    MIT
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    Provides a generic HTTP client tool allowing AI agents to make arbitrary HTTP requests from their environment, with support for environment-variable placeholders for secrets.
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    A local MCP server that scans repository dependencies for known vulnerabilities (CVEs) using OSV.dev, enriches findings with NVD and CISA KEV data, and supports triage, remediation, and accepted risk management directly from an AI coding assistant.
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    MIT
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    Security scanning for MCP servers from the inside out. Provides runtime inspection, AST-based static analysis, config audit, dependency analysis, and OWASP MCP Top 10 compliance in a single MCP server.
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    MIT
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    Enables comprehensive security reconnaissance, vulnerability assessment, and threat intelligence gathering by integrating Shodan's API. It provides tools for searching internet-connected devices, performing DNS operations, and querying the Shodan exploit database.
    11
    Apache 2.0
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    Provides AI agents with 37 OSINT tools and 12 data sources to perform unified reconnaissance, domain analysis, and attack surface mapping. It enables agents to query, correlate, and reason across platforms like Shodan, VirusTotal, and Censys in parallel.
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    MIT
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    A comprehensive reconnaissance toolset that provides AI agents with 37 tools across 12 data sources like Shodan and VirusTotal for automated intelligence gathering. It enables agents to perform domain reconnaissance, attack surface mapping, and cross-platform data correlation within a single conversational interface.
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    MIT
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    Provides access to Shodan API functionality, enabling AI assistants to query information about internet-connected devices for cybersecurity research and threat intelligence.
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    MIT
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    The most comprehensive steganography analysis toolkit for AI agents, providing 60 tools across 7 categories for detecting and extracting hidden data from images, audio, text, and files, all running 100% offline.
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    MIT
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    Enables testing AI safety classifier robustness against query decomposition, obfuscation, and multi-agent attacks. Provides tools for full evaluation pipelines, query previews, and status checks.
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    MIT
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    Enables security professionals to query and analyze Active Directory attack paths from BloodHound Community Edition data using natural language through Claude Desktop's Model Context Protocol interface.
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    GPL 3.0
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    Enables users to query BloodHound Active Directory graph data using natural language, finding attack paths, Kerberoastable accounts, and other AD security insights.
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