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cotlib is a secure, high-performance Go library for parsing, validating, and generating Cursor-on-Target (CoT) XML messages. It features a comprehensive, embedded type catalog with metadata and XSD catalogue, robust validation logic, and LLM/AI-friendly search APIs. Designed for reliability, composability, and security.

AI-powered security operations for Wazuh SIEM—use any MCP-compatible client to ask security questions in plain English. Faster threat detection, incident triage, and compliance checks with real-time monitoring and anomaly spotting. Production-ready MCP server for conversational SOC workflows.

Provide secure and detailed analysis of your BingX trading data through a specialized AI agent. Fetch historical trades, analyze trading performance with key metrics like win rate and P&L, and test API connectivity seamlessly. Enhance your trading insights with easy integration and secure credential management.

MCP server for APIMesh — 25 x402-payable API tools for AI agents. Web vitals, security headers, SEO audit, email security, email verify, tech stack detection, brand assets, redirect chains, indexability, brand availability, HTTP status, favicon, health ch

Analyze domain infrastructure by performing comprehensive DNS, WHOIS, and security audits. Identify technology stacks, track SSL validity, and verify email security configurations to ensure optimal web presence. Monitor global DNS propagation and discover subdomains to gain a complete picture of any domain's health and history.

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

This Guidance demonstrates how to securely run Model Context Protocol (MCP) servers on the AWS Cloud using containerized architecture. It helps organizations implement industry-standard OAuth 2.0 authentication while protecting server deployments with multiple security layers, including content delivery networks and web application firewalls.

The Model Context Protocol (MCP) Atlassian integration is an open-source implementation that bridges Atlassian products (Jira and Confluence) with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Atlassian tools while maintaining data privacy and security. Key features include:

Detect live website vulnerabilities and security flaws in GitHub repositories using automated DAST and SAST scanning. Safeguard applications by identifying exposed secrets, insecure dependencies, and common code patterns prone to exploitation. Receive structured fix plans with precise code remediation steps to resolve identified risks and improve security posture.

Provide secure, permission-controlled access to Firebase Firestore databases through a standardized MCP interface. Enable AI assistants and other clients to perform full CRUD operations, advanced queries, batch operations, and transactional updates with granular collection and operation-level permissions. Ensure data consistency and security with default deny rules and fine-grained access control.

A comprehensive MCP server for scanning and analyzing MESH by Viscount systems for default credential vulnerabilities. This tool is designed for security research and educational purposes only. ## 🚨 Important Notice **This tool is for educational and security research purposes only.** Unauthorized access to systems is illegal. Always obtain proper authorization before scanning any systems. ## Features ### 🔍 Scanning Capabilities - **Single IP Scanning**: Scan individual IP addresses for ME

[](https://glama.ai/mcp/servers/agentgraph-co/agentgraph) 🐍 ☁️ 🍎 🪟 🐧 - Trust verification and security scanning for AI agents. Checks security posture of third-party MCP servers and tools with signed attestations (Ed25519/JWS) before interaction.