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Threat Modeling MCP Server

Threat Modeling MCP Server

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      Enables AI-powered threat modeling with tools for creating threat models, analyzing security threats, generating security controls, and validating architecture against best practices.
      -
    • A
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      Enables developers and product owners to scan a codebase and extract a readable business overview—features, roles, access control, data model, and endpoints—along with findings on missing or inconsistent rules, and generate shareable HTML or Markdown reports and diagrams.
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      Turns a description of an AI or agentic system's architecture into ranked adversary techniques, coverage-ordered mitigations with assigned owners and components, compounding risks, and real-world incidents of similar shape. Also provides reference lookup and cross-mappings to ATT&CK, STRIDE, OWASP Top 10 for LLM, and NIST AI RMF, entirely offline.
      Apache 2.0
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      Enables read-only, evidence-led security audits of GitHub-hosted source code through explicit workflow primitives: repository reconnaissance, coverage-ledger planning, heuristic candidate leads, structured findings validation, independent verification gates, and JSON/Markdown report generation. It never clones, executes, or probes the target code, and marks runs incomplete until coverage and reviewer-verification gates pass.
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      AGPL 3.0
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      Automates 85-95% of the Secure Software Development Lifecycle (SSDLC) planning phase through multi-role AI orchestration, enabling business analysis, threat modeling, test strategy design, and security code review.
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      Enables AI-powered security scanning of codebases through conversational analysis, allowing users to assess, threat model, code review, DAST test, and generate security reports using natural language with Claude.
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    TDQS

    B3.1/5.0

    Scored across 11 tools

    Disambiguation5/5

    Each tool targets a distinct threat-modeling domain or lifecycle action: assumptions, workflow orchestration, system context, architecture, actors, trust boundaries, asset flows, threats/mitigations, code validation, data-model inspection, and export. The manage_* tools share a verb but have clearly separate nouns and descriptions, so an agent should not confuse them.

    Naming Consistency5/5

    All tool names use snake_case and a consistent verb_noun pattern: manage_*, inspect_data_models, and export_threat_model. The repetition of manage_* is predictable rather than confusing.

    Tool Count5/5

    Eleven tools is well-scoped for a threat-modeling workflow. The count reflects distinct lifecycle phases and entities without obvious redundancy or missing operations.

    Completeness5/5

    The surface covers the full threat-modeling lifecycle: assumptions, workflow, business context, architecture, actors, trust boundaries, asset flows, threats and mitigations, code validation, data-model inspection, and export. There are no obvious dead ends for the stated domain.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues