{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-0x4m4-hexstrike-ai","slug":"mcp-0x4m4-hexstrike-ai","name":"hexstrike-ai","type":"mcp","url":"https://github.com/0x4m4/hexstrike-ai","page_url":"https://unfragile.ai/mcp-0x4m4-hexstrike-ai","categories":["mcp-servers","code-editors","code-review-security"],"tags":["0x4m4","ai","ai-agents","ai-cybersecurity","ai-hacking","ai-penetration-testing","ai-security-tool","artificial-intelligence","ctf-tools","generative-ai","hexstrike","kali-linux","kali-tools","llm","llm-integration","mcp","mcp-server","mcp-tools","pentesting","pentesting-tools"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-0x4m4-hexstrike-ai__cap_0","uri":"capability://tool.use.integration.mcp.based.security.tool.orchestration.with.150.integrated.tools","name":"mcp-based security tool orchestration with 150+ integrated tools","description":"Exposes 150+ professional cybersecurity tools (nmap, gobuster, nuclei, sqlmap, ghidra, prowler, etc.) through the Model Context Protocol (MCP) as decorated @mcp.tool functions in hexstrike_mcp.py. External AI agents (Claude, GPT, Copilot) invoke tools via standardized MCP protocol, which routes requests through a Flask-based REST API server (hexstrike_server.py) that executes commands and returns structured results. The architecture decouples LLM agents from direct tool execution, enabling multi-agent orchestration with intelligent parameter optimization.","intents":["I want my AI agent to autonomously run penetration testing tools without direct shell access","I need to expose security tools to Claude/GPT through a standardized protocol for automated security workflows","I want to build a multi-agent system where different AI agents can coordinate tool execution for complex security assessments"],"best_for":["Security researchers and penetration testers building autonomous LLM-based testing workflows","Bug bounty hunters automating reconnaissance and vulnerability discovery with AI agents","Teams integrating professional security tools into AI-driven security platforms"],"limitations":["Requires all 150+ tools to be pre-installed on the host system (nmap, gobuster, nuclei, sqlmap, ghidra, prowler, etc.) — no containerization abstraction provided","MCP protocol communication adds latency for each tool invocation; no built-in batching or parallel execution optimization","Tool output parsing relies on regex/text parsing rather than structured APIs, making results fragile to tool version changes","No built-in sandboxing — executed tools run with the privileges of the hexstrike process"],"requires":["Python 3.9+","FastMCP library for MCP protocol implementation","Flask for REST API server","All 150+ security tools installed and in system PATH (nmap, gobuster, nuclei, sqlmap, ghidra, prowler, metasploit, etc.)","API credentials for cloud security tools (AWS/Azure/GCP for prowler)","MCP-compatible AI client (Claude, GPT-4, VS Code Copilot with MCP support)"],"input_types":["natural language commands from AI agents","structured JSON parameters for tool configuration","target specifications (IP addresses, domains, URLs)"],"output_types":["structured JSON results from tool execution","raw command output (stdout/stderr)","parsed vulnerability findings","network topology maps"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_1","uri":"capability://planning.reasoning.intelligent.target.analysis.and.tool.selection.engine","name":"intelligent target analysis and tool selection engine","description":"Implements POST /api/intelligence/analyze-target and POST /api/intelligence/select-tools endpoints that use AI-powered profiling to automatically recommend which security tools to execute based on target characteristics. The system analyzes target metadata (IP ranges, domain structure, cloud provider, application stack) and generates a ranked list of applicable tools with context-aware parameters. This eliminates manual tool selection and enables adaptive pentesting workflows where tool chains adjust based on discovered vulnerabilities.","intents":["I want the AI agent to automatically determine which security tools are most relevant for a given target without me specifying them","I need intelligent parameter tuning for tools based on target characteristics (e.g., different nmap flags for cloud vs. on-prem)","I want to reduce false positives by running only applicable tools for the target's technology stack"],"best_for":["Penetration testers who want to automate tool selection decisions","Bug bounty hunters running large-scale reconnaissance across diverse targets","Security teams building self-driving vulnerability assessment workflows"],"limitations":["Tool selection quality depends on accuracy of target profiling — misidentified technologies lead to irrelevant tool recommendations","No feedback loop to learn from past tool selections; recommendations are stateless","Requires pre-trained AI models or heuristics for tool-to-target mapping; no published training data or model weights","Cannot recommend tools for novel/emerging technologies not in the training dataset"],"requires":["Target metadata (IP address, domain, cloud provider, application fingerprints)","AI model access (Claude, GPT, or local LLM) for profiling logic","Pre-configured tool registry with metadata (supported targets, parameter templates)"],"input_types":["JSON target profile (IP, domain, cloud provider, detected services)","natural language target description"],"output_types":["ranked list of recommended tools with confidence scores","optimized parameter sets for each tool","execution order/dependency graph"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_10","uri":"capability://tool.use.integration.sql.injection.testing.with.sqlmap.automation.and.parameter.optimization","name":"sql injection testing with sqlmap automation and parameter optimization","description":"Exposes sqlmap_scan() MCP tool that automates SQL injection vulnerability testing with intelligent parameter optimization. The tool automatically detects injectable parameters, tests multiple injection techniques (UNION-based, blind, time-based), and extracts database information. Integration with the intelligence engine enables context-aware tuning (e.g., aggressive testing for development targets, stealthy testing for production). Results include vulnerability confirmation, database schema extraction, and exploitation proof-of-concept.","intents":["I want to automatically test web applications for SQL injection vulnerabilities","I need to extract database information from vulnerable applications","I want to generate SQL injection proof-of-concept code for vulnerability reports"],"best_for":["Web application penetration testers automating SQL injection testing","Bug bounty hunters discovering SQL injection vulnerabilities","Security teams conducting continuous web application assessment"],"limitations":["Sqlmap requires valid HTTP requests as input; cannot generate requests from scratch","Parameter detection is heuristic-based; may miss non-obvious injectable parameters","Database extraction is slow for large databases; no sampling or filtering mechanisms","WAF/IPS evasion is limited; scans may be blocked by security controls"],"requires":["Sqlmap installed and in system PATH","Target URL with injectable parameters","HTTP request template (URL, POST data, headers)","Database credentials (optional, for authenticated testing)"],"input_types":["target URL with parameters","HTTP request template (GET/POST)","optional sqlmap parameters (technique, risk level, etc.)"],"output_types":["JSON vulnerability confirmation (injectable parameters, techniques)","database schema extraction (tables, columns, data types)","extracted data samples","exploitation proof-of-concept SQL queries"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_11","uri":"capability://tool.use.integration.binary.analysis.and.reverse.engineering.with.ghidra.integration","name":"binary analysis and reverse engineering with ghidra integration","description":"Exposes ghidra_analyze() MCP tool that automates binary analysis and reverse engineering using Ghidra's decompilation engine. The tool analyzes binaries to extract function signatures, identify vulnerabilities (buffer overflows, format strings, use-after-free), and generate decompiled source code. Integration with the intelligence engine enables context-aware analysis (e.g., focusing on network-facing functions for network services, authentication functions for security-critical binaries). Results include vulnerability findings, function call graphs, and decompiled code snippets.","intents":["I want to automatically analyze binaries for vulnerabilities without manual reverse engineering","I need to extract function signatures and call graphs from compiled code","I want to identify security-critical functions and potential exploitation vectors"],"best_for":["Security researchers analyzing malware and vulnerable binaries","Penetration testers identifying vulnerabilities in compiled applications","CTF competitors solving reverse engineering challenges"],"limitations":["Ghidra decompilation is heuristic-based; generated code may be inaccurate or incomplete","Binary analysis requires matching architecture and OS; cross-architecture analysis is limited","Vulnerability detection is pattern-based; novel vulnerability types may be missed","Analysis time scales with binary size; large binaries may timeout"],"requires":["Ghidra installed and in system PATH","Target binary (ELF, PE, Mach-O, etc.)","Matching architecture and OS (x86, x64, ARM, etc.)","Optional debug symbols for improved analysis"],"input_types":["binary file path","architecture specification (x86, x64, ARM, etc.)","optional analysis parameters (focus areas, function names)"],"output_types":["JSON vulnerability findings (buffer overflows, format strings, etc.)","function call graphs","decompiled source code snippets","function signatures and prototypes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_12","uri":"capability://tool.use.integration.cloud.security.assessment.with.prowler.integration.for.aws.azure.gcp","name":"cloud security assessment with prowler integration for aws/azure/gcp","description":"Exposes prowler_assess() MCP tool that automates cloud security assessment for AWS, Azure, and GCP environments. The tool runs 200+ security checks against cloud infrastructure, identifying misconfigurations, compliance violations, and security risks. Integration with the intelligence engine enables context-aware assessment (e.g., focusing on identity/access checks for AWS, network security checks for Azure). Results include compliance status (CIS, PCI-DSS, HIPAA), risk ratings, and remediation recommendations.","intents":["I want to automatically assess cloud infrastructure security without manual configuration review","I need to check compliance with security standards (CIS, PCI-DSS, HIPAA) in cloud environments","I want to identify cloud misconfigurations and security risks at scale"],"best_for":["Cloud security teams assessing AWS/Azure/GCP infrastructure","Penetration testers evaluating cloud security posture","Organizations conducting cloud compliance audits"],"limitations":["Prowler requires cloud provider credentials with appropriate permissions; limited by IAM policies","Assessment scope is limited to cloud APIs; cannot assess application-level security","Compliance checks are point-in-time; no continuous monitoring or trend analysis","False positives are common; manual review of findings required"],"requires":["Prowler installed and in system PATH","Cloud provider credentials (AWS access keys, Azure service principal, GCP service account)","Appropriate IAM permissions for security assessment (read-only access to resources)"],"input_types":["cloud provider specification (aws, azure, gcp)","cloud credentials (access keys, service principal, etc.)","optional assessment parameters (compliance framework, regions, etc.)"],"output_types":["JSON assessment results with compliance status","risk ratings and severity scores","misconfigurations and security findings","remediation recommendations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_13","uri":"capability://data.processing.analysis.structured.result.parsing.and.vulnerability.aggregation","name":"structured result parsing and vulnerability aggregation","description":"Implements result parsing and aggregation logic that converts heterogeneous tool outputs (nmap XML, nuclei JSON, sqlmap text, ghidra binary analysis) into a unified vulnerability data model. The system deduplicates findings across tools, assigns severity scores, and generates structured reports. Parsing uses tool-specific parsers (regex, XML parsing, JSON extraction) that normalize results into a common schema with vulnerability type, affected asset, severity, and remediation guidance.","intents":["I want to aggregate findings from multiple tools into a single unified report","I need to deduplicate vulnerabilities discovered by different tools","I want to generate structured vulnerability reports with consistent formatting"],"best_for":["Security teams consolidating findings from multiple scanning tools","Vulnerability management platforms aggregating results from diverse sources","Reporting systems generating unified security assessments"],"limitations":["Parsing is tool-specific; adding new tools requires custom parser implementation","Deduplication is heuristic-based; similar vulnerabilities may not be recognized as duplicates","Severity scoring is tool-dependent; different tools may assign different scores to the same vulnerability","No built-in false positive filtering; manual review required"],"requires":["Tool-specific output parsers (nmap XML, nuclei JSON, sqlmap text, etc.)","Unified vulnerability data model/schema","Deduplication logic and heuristics","Severity scoring rules"],"input_types":["raw tool outputs (nmap XML, nuclei JSON, sqlmap text, etc.)"],"output_types":["JSON unified vulnerability report","deduplicated findings list","severity-ranked vulnerability list","structured report with remediation guidance"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_14","uri":"capability://planning.reasoning.natural.language.security.assessment.instructions.with.ai.interpretation","name":"natural language security assessment instructions with ai interpretation","description":"Enables users to provide security assessment objectives in natural language (e.g., 'Find all SQL injection vulnerabilities in the web application and generate proof-of-concept exploits'), which the AI agent interprets and decomposes into a sequence of tool invocations. The system uses Claude/GPT to understand assessment intent, map it to available tools, and generate execution plans. This abstraction layer eliminates the need for users to know specific tool names or parameters, enabling non-experts to conduct security assessments.","intents":["I want to describe security assessment objectives in plain English without knowing specific tool names","I need the AI agent to automatically translate my assessment goals into tool execution plans","I want to conduct security assessments without deep technical knowledge of pentesting tools"],"best_for":["Non-technical security managers defining assessment scope","Developers integrating security testing into CI/CD pipelines","Organizations automating security assessments without dedicated security staff"],"limitations":["Natural language interpretation is ambiguous; unclear instructions may result in incorrect tool selection","No feedback loop to clarify ambiguous instructions; system makes best-guess interpretations","Complex assessment objectives may be misunderstood or oversimplified","Requires AI model access (Claude, GPT-4); adds latency and cost"],"requires":["AI model access (Claude, GPT-4, or local LLM)","Tool registry with descriptions and capabilities","Prompt engineering for assessment interpretation"],"input_types":["natural language assessment objective (text)"],"output_types":["tool execution plan (sequence of tool invocations with parameters)","assessment results and findings"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_2","uri":"capability://automation.workflow.autonomous.bug.bounty.hunting.workflow.automation","name":"autonomous bug bounty hunting workflow automation","description":"Implements BugBountyWorkflowManager that orchestrates a multi-stage reconnaissance and vulnerability discovery pipeline: reconnaissance → service enumeration → vulnerability scanning → exploitation → reporting. The manager chains tools (nmap, gobuster, nuclei, sqlmap) with AI-driven decision logic between stages, automatically escalating findings and adapting the workflow based on discovered vulnerabilities. Each stage outputs structured findings that feed into the next stage's tool selection, creating a closed-loop autonomous pentesting loop.","intents":["I want to automate the entire bug bounty hunting process from reconnaissance to vulnerability reporting","I need the AI agent to autonomously decide when to escalate from reconnaissance to exploitation based on findings","I want to run continuous reconnaissance on bug bounty targets with minimal manual intervention"],"best_for":["Bug bounty hunters managing multiple targets simultaneously","Security teams automating initial reconnaissance for manual follow-up","Platforms building automated bug bounty services"],"limitations":["Workflow is linear (reconnaissance → enumeration → scanning → exploitation) with no branching for complex attack paths","No built-in scope management — cannot automatically respect target boundaries or out-of-scope assets","Exploitation stage requires manual approval or pre-configured exploitation rules; no autonomous exploit execution","Results aggregation is tool-specific; no unified vulnerability database or deduplication across tools"],"requires":["Target domain/IP address","Bug bounty program scope definition","Credentials for vulnerability databases (NVD, CVE feeds) for nuclei templates","Configured tool parameters for reconnaissance tools (nmap, gobuster)","AI agent with decision-making capability (Claude, GPT-4)"],"input_types":["target domain or IP range","scope definition (in-scope assets, excluded paths)","natural language instructions for workflow customization"],"output_types":["structured vulnerability report with CVSS scores","reconnaissance findings (open ports, services, subdomains)","exploitation proof-of-concept results","remediation recommendations"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_3","uri":"capability://planning.reasoning.ctf.challenge.solving.with.autonomous.reasoning","name":"ctf challenge solving with autonomous reasoning","description":"Implements CTFWorkflowManager that decomposes Capture-The-Flag challenges into sub-tasks (binary analysis, web exploitation, cryptography, forensics) and autonomously selects appropriate tools (ghidra for reverse engineering, sqlmap for web exploits, steghide for forensics). The manager uses AI reasoning to interpret challenge descriptions, map them to tool capabilities, and chain tools to solve multi-stage challenges. Results from each tool feed into the next stage's analysis, enabling complex challenge solving without explicit step-by-step instructions.","intents":["I want the AI agent to automatically solve CTF challenges by analyzing the challenge description and selecting appropriate tools","I need to automate binary analysis, web exploitation, and forensics tasks for CTF competitions","I want to generate CTF solutions that explain the reasoning behind each tool selection"],"best_for":["CTF competitors automating routine challenge solving tasks","Security training platforms providing AI-assisted CTF solutions","Teams building CTF challenge generators with automated solution verification"],"limitations":["Challenge interpretation relies on natural language understanding; ambiguous or novel challenge types may be misclassified","Tool output parsing is challenge-specific; generic parsing fails for non-standard challenge formats","No built-in flag validation — cannot automatically verify if extracted flags are correct","Limited to tools in the arsenal; challenges requiring specialized/custom tools cannot be solved"],"requires":["Challenge description (text, binary, or web application)","Challenge category hints (binary, web, crypto, forensics, etc.)","Reverse engineering tools (ghidra, radare2, IDA Pro equivalent)","Web exploitation tools (sqlmap, burp suite equivalent)","Forensics tools (steghide, exiftool, binwalk)"],"input_types":["CTF challenge description (text)","challenge binary or web application","challenge category classification"],"output_types":["extracted flag","step-by-step solution explanation","tool execution logs","intermediate analysis results"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_4","uri":"capability://planning.reasoning.advanced.vulnerability.research.with.adaptive.tool.chaining","name":"advanced vulnerability research with adaptive tool chaining","description":"Implements VulnerabilityResearchManager that conducts deep vulnerability analysis by chaining multiple tools (nuclei with custom templates, ghidra for binary analysis, sqlmap for injection testing) with feedback loops. The manager discovers vulnerabilities, analyzes their root causes using reverse engineering, tests exploitation paths, and generates detailed technical reports. AI reasoning determines which tools to apply based on vulnerability type, enabling adaptive research workflows that adjust based on findings.","intents":["I want to conduct in-depth vulnerability analysis that goes beyond surface-level scanning","I need to automatically generate technical vulnerability reports with root cause analysis","I want to test exploitation paths and generate proof-of-concept code for discovered vulnerabilities"],"best_for":["Security researchers conducting vulnerability research and analysis","Vulnerability disclosure teams generating detailed technical reports","Security vendors building vulnerability intelligence platforms"],"limitations":["Root cause analysis requires access to source code or binaries; black-box analysis is limited","Exploitation testing may trigger security alerts or cause service disruption; requires careful scope management","Report generation is template-based; custom analysis or novel vulnerability types require manual intervention","No built-in vulnerability deduplication; similar vulnerabilities across targets may be reported separately"],"requires":["Target application or binary","Source code (optional, for deeper analysis)","Reverse engineering tools (ghidra, radare2)","Vulnerability scanning tools (nuclei with templates)","Exploitation testing tools (sqlmap, custom exploits)","Report generation templates"],"input_types":["target application or binary","vulnerability description or CVE identifier","source code (optional)"],"output_types":["detailed vulnerability analysis report","root cause analysis","exploitation proof-of-concept","remediation recommendations","CVSS score and severity assessment"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_5","uri":"capability://tool.use.integration.rest.api.command.execution.with.health.monitoring.and.telemetry","name":"rest api command execution with health monitoring and telemetry","description":"Exposes POST /api/command endpoint that executes arbitrary security commands with real-time output streaming, combined with GET /health for server status and tool availability checks, and GET /api/telemetry for performance metrics. The Flask-based server manages command execution lifecycle, captures stdout/stderr, and tracks execution time and resource usage. Health checks verify that all 150+ tools are installed and accessible, enabling proactive detection of missing dependencies.","intents":["I want to execute security commands through a REST API without direct shell access","I need to monitor which tools are available and healthy on the hexstrike server","I want to track performance metrics and execution statistics for security tool runs"],"best_for":["Teams building security automation platforms with REST API interfaces","Cloud-based security services that need to expose tool execution as APIs","Monitoring systems tracking security tool availability and performance"],"limitations":["No built-in command sandboxing or resource limits; malicious commands can consume unlimited CPU/memory","Command output is streamed as plain text; no structured parsing or result validation","Health checks only verify tool existence in PATH; cannot detect version mismatches or configuration issues","Telemetry is in-memory only; no persistence or historical trend analysis"],"requires":["Flask web framework","Python 3.9+","All security tools installed and in system PATH","HTTP client to invoke REST endpoints"],"input_types":["command string (e.g., 'nmap -sV target.com')","command parameters as JSON"],"output_types":["command stdout/stderr as text","JSON health status (tool availability)","JSON telemetry (execution time, resource usage)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_6","uri":"capability://automation.workflow.caching.and.performance.optimization.for.repeated.tool.executions","name":"caching and performance optimization for repeated tool executions","description":"Implements GET /api/cache/stats endpoint that tracks cache performance and statistics for repeated security tool executions. The system caches tool outputs (nmap scans, directory enumeration results, vulnerability findings) to avoid redundant execution of expensive operations. Cache keys are based on tool name, target, and parameters, enabling intelligent reuse of previous results when scanning the same target multiple times or across different agents.","intents":["I want to avoid re-running expensive scans (nmap, nuclei) when the same target is scanned multiple times","I need to understand cache hit rates and performance improvements from caching","I want to share scan results across multiple AI agents without re-executing tools"],"best_for":["Teams running continuous security assessments on the same targets","Multi-agent systems where different agents need the same scan results","Resource-constrained environments where tool execution is expensive"],"limitations":["Cache invalidation is time-based only; no mechanism to detect when targets change (e.g., new services deployed)","Cache is in-memory and not persisted; lost on server restart","No cache size limits or eviction policies; unbounded memory growth possible","Cache statistics are read-only; no manual cache invalidation or warming"],"requires":["In-memory cache implementation (Python dict or similar)","Cache key generation logic based on tool + target + parameters","TTL (time-to-live) configuration for cache entries"],"input_types":["cache statistics query (no parameters)"],"output_types":["JSON cache statistics (hit rate, miss rate, size, entries)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_7","uri":"capability://planning.reasoning.multi.agent.coordination.and.autonomous.decision.making","name":"multi-agent coordination and autonomous decision-making","description":"Implements 12+ specialized AI agents (reconnaissance agent, exploitation agent, analysis agent, reporting agent, etc.) that operate autonomously within the HexStrike framework, each with specific responsibilities and decision-making logic. Agents communicate through a shared context and decision bus, coordinating tool execution and result analysis. The system uses AI reasoning (Claude, GPT) to enable agents to make autonomous decisions about which tools to run, when to escalate findings, and how to adapt workflows based on discovered vulnerabilities.","intents":["I want multiple AI agents to work together autonomously on a security assessment without manual coordination","I need agents to make independent decisions about tool selection and workflow progression","I want to scale security assessments across multiple agents working in parallel"],"best_for":["Large-scale security assessments requiring parallel tool execution","Teams building multi-agent security automation platforms","Organizations conducting continuous security monitoring with autonomous agents"],"limitations":["Agent coordination relies on shared context; no distributed consensus mechanism for conflicting decisions","No built-in conflict resolution when agents make contradictory decisions (e.g., different exploitation strategies)","Agent state is not persisted; workflow progress is lost on system restart","Scaling beyond 12 agents not tested; performance degradation with many concurrent agents unknown"],"requires":["AI model access (Claude, GPT-4) for each agent's decision-making","Shared context store for inter-agent communication","Agent role definitions and responsibility boundaries","Decision-making rules or heuristics for each agent"],"input_types":["security assessment objectives","target specifications","agent role assignments"],"output_types":["autonomous agent decisions and actions","coordinated tool execution results","aggregated findings and recommendations"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_8","uri":"capability://tool.use.integration.network.reconnaissance.with.advanced.nmap.integration.and.optimization","name":"network reconnaissance with advanced nmap integration and optimization","description":"Exposes nmap_scan() MCP tool that executes advanced Nmap scans with optimization for different target types (cloud infrastructure, on-premises networks, IoT devices). The tool automatically selects appropriate scan types (SYN stealth scans, UDP scans, service version detection) based on target characteristics, applies timing templates for network conditions, and parses results into structured JSON. Integration with the intelligence engine enables context-aware parameter tuning (e.g., aggressive scanning for cloud targets, stealthy scanning for defended networks).","intents":["I want to run optimized nmap scans that adapt to target characteristics without manual parameter tuning","I need to discover open ports, services, and versions on a target network","I want to integrate nmap results into automated vulnerability assessment workflows"],"best_for":["Penetration testers automating network reconnaissance","Bug bounty hunters discovering services on target domains","Security teams conducting continuous network monitoring"],"limitations":["Nmap output parsing is regex-based; changes in nmap output format break parsing","Scan timing is fixed (T3 template); no dynamic adjustment based on network conditions","UDP scanning is disabled by default due to performance; requires explicit parameter override","No built-in firewall detection or evasion techniques; scans may be blocked by IDS/IPS"],"requires":["Nmap installed and in system PATH","Network access to target (no firewall blocking)","Appropriate permissions for raw socket access (may require root/admin)"],"input_types":["target IP address or CIDR range","scan type specification (SYN, UDP, service detection, etc.)","optional nmap parameters"],"output_types":["JSON structured results with open ports, services, versions","raw nmap XML output","service fingerprints"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-0x4m4-hexstrike-ai__cap_9","uri":"capability://tool.use.integration.web.application.security.scanning.with.gobuster.and.nuclei.integration","name":"web application security scanning with gobuster and nuclei integration","description":"Exposes gobuster_scan() and nuclei_scan() MCP tools that automate web application reconnaissance and vulnerability scanning. Gobuster performs directory and file enumeration with customizable wordlists and filtering, while nuclei runs vulnerability templates against discovered endpoints. The tools integrate with the intelligence engine to select appropriate wordlists and templates based on target technology stack (e.g., WordPress-specific templates for WordPress sites). Results are parsed into structured JSON and feed into downstream exploitation workflows.","intents":["I want to automatically discover hidden directories and files on web applications","I need to scan web applications for known vulnerabilities using nuclei templates","I want to adapt scanning parameters based on detected technology stack (WordPress, Drupal, custom frameworks)"],"best_for":["Web application penetration testers automating reconnaissance","Bug bounty hunters scanning web targets for vulnerabilities","Security teams conducting continuous web application monitoring"],"limitations":["Gobuster wordlist selection is static; no dynamic wordlist generation based on target context","Nuclei templates are version-specific; outdated templates may miss vulnerabilities or generate false positives","No built-in WAF detection or bypass techniques; scans may be blocked by web application firewalls","Results are tool-specific; no unified vulnerability database or deduplication across tools"],"requires":["Gobuster installed and in system PATH","Nuclei installed with template database","Wordlists for directory enumeration (SecLists or custom)","Nuclei templates for vulnerability scanning","Target web application URL"],"input_types":["target URL","wordlist selection (common, large, custom)","nuclei template selection (all, category-specific, custom)","optional scanning parameters (threads, timeout, etc.)"],"output_types":["JSON list of discovered directories and files","JSON list of vulnerabilities with severity scores","raw tool output (optional)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","FastMCP library for MCP protocol implementation","Flask for REST API server","All 150+ security tools installed and in system PATH (nmap, gobuster, nuclei, sqlmap, ghidra, prowler, metasploit, etc.)","API credentials for cloud security tools (AWS/Azure/GCP for prowler)","MCP-compatible AI client (Claude, GPT-4, VS Code Copilot with MCP support)","Target metadata (IP address, domain, cloud provider, application fingerprints)","AI model access (Claude, GPT, or local LLM) for profiling logic","Pre-configured tool registry with metadata (supported targets, parameter templates)","Sqlmap installed and in system PATH"],"failure_modes":["Requires all 150+ tools to be pre-installed on the host system (nmap, gobuster, nuclei, sqlmap, ghidra, prowler, etc.) — no containerization abstraction provided","MCP protocol communication adds latency for each tool invocation; no built-in batching or parallel execution optimization","Tool output parsing relies on regex/text parsing rather than structured APIs, making results fragile to tool version changes","No built-in sandboxing — executed tools run with the privileges of the hexstrike process","Tool selection quality depends on accuracy of target profiling — misidentified technologies lead to irrelevant tool recommendations","No feedback loop to learn from past tool selections; recommendations are stateless","Requires pre-trained AI models or heuristics for tool-to-target mapping; no published training data or model weights","Cannot recommend tools for novel/emerging technologies not in the training dataset","Sqlmap requires valid HTTP requests as input; cannot generate requests from scratch","Parameter detection is heuristic-based; may miss non-obvious injectable parameters","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6691077187638061,"quality":0.6,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-04-27T08:56:07Z"},"community":{"stars":8486,"forks":1841,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-0x4m4-hexstrike-ai","compare_url":"https://unfragile.ai/compare?artifact=mcp-0x4m4-hexstrike-ai"}},"signature":"Df4ffLTqou9CH3gXyl2C0xNe/qGkAg1xx+oMr+rAq7rYQPiNYDmwTLzerZrvt6ihsqPaOqzzFS1YLYiCCM3xBA==","signedAt":"2026-06-20T16:16:12.572Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-0x4m4-hexstrike-ai","artifact":"https://unfragile.ai/mcp-0x4m4-hexstrike-ai","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-0x4m4-hexstrike-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}