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Alternatives to llm-assure-mcp

No user-submitted related servers found.

    Related Servers

    • A
      license
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      quality
      C
      maintenance
      Enables AI assistants to conduct penetration testing and offensive security workflows, including reconnaissance, exploitation, post-exploitation, threat intelligence, malware analysis, forensics, and reporting through MCP tools. It supports session management, C2, evasion, credential attacks, and many specialized security testing capabilities.
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    • A
      license
      A
      quality
      B
      maintenance
      Enables AI assistants to perform authorized web security assessments through MCP tools and a guided review prompt, including passive and full scans, WAF detection, remediation fixes, report export, triage, scan comparison, and proof-of-control verification.
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      66 PyPI
      5
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to orchestrate 100+ security tools over MCP for authorized penetration testing, including recon, scanning, exploitation, attack-chain planning, and knowledge-base retrieval.
      7
      Apache 2.0
    • A
      license
      C
      quality
      D
      maintenance
      Integrates AI safety analysis, red-teaming, and prompt auditing directly into MCP-compatible clients like Claude Desktop and Cursor IDE, allowing real-time analysis of prompts and detection of jailbreak attempts.
      28
      4
      Apache 2.0
    • A
      license
      Not graded
      quality
      D
      maintenance
      AI red teaming toolkit exposed as a Model Context Protocol (MCP) server. Connect any MCP client and test AI systems for safety vulnerabilities.
      MIT

    TDQS

    A3.8/5.0

    Scored across 5 tools

    Disambiguation5/5

    The tools split cleanly by resource and action: suites and probes are listed separately, automated suite execution is distinct from single adaptive prompt sending, and findings retrieval is clearly separate from running an assessment. Descriptions reinforce the boundary between run_assessment and send_probe by emphasizing automated reporting versus multi-step manual probing.

    Naming Consistency5/5

    All tool names follow a consistent snake_case verb_noun pattern: list_suites, run_assessment, list_probes, send_probe, get_findings. There are no mixed conventions or vague names.

    Tool Count5/5

    Five tools is well-scoped for a red-team assessment server: discovery, execution, manual probing, and findings extraction are each represented without bloat. No tool feels redundant or missing from the core workflow.

    Completeness5/5

    The surface covers the assessment lifecycle: inspect suites, inspect probes, run automated assessments with reports, perform adaptive manual probes, and retrieve filtered findings. Target configuration is handled through file or inline arguments, so no obvious dead ends remain for the stated purpose.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues