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204,009 tools. Last updated 2026-06-14 22:14

"OWASP" matching MCP tools:

  • Scan a public GitHub MCP-server repository for security issues. Clones the repo (shallow, <60s, <200 MB), runs compuute-scan v0.6.2 in static analysis mode (no code execution from the target), and returns a structured report with severity counts, a 0-100 score, and the 10 most severe findings. WHEN TO USE: - Before connecting to an unknown MCP server discovered via Anthropic Registry, Smithery, mcp.so, or a Discord recommendation. - Before installing a third-party MCP-server package into a production pipeline. - As part of an agent's pre-commit / pre-deploy due-diligence step when adding new dependencies. - As one input to a multi-source trust evaluation (combine with publisher reputation, package install count, last-update recency). WHEN NOT TO USE: - For private repos. Use the on-prem CLI instead: `npx compuute-scan ./path-to-private-repo` - For deep exploitability assessment of a specific code path. This is pattern matching, not dataflow analysis. Book a manual L2-L4 audit at https://compuute.se/audit for that depth. - For non-GitHub hosts (GitLab, Bitbucket, self-hosted). v1 supports github.com only. - For repos > 200 MB or clone time > 60s. The endpoint returns a 413 or 504 in those cases — fall back to local CLI. EXPECTED RESPONSE TIME: - Median: ~1-2 seconds for small repos (<100 files). - p99: ~10 seconds for medium repos. - Hard timeout at clone=60s, scan=120s combined. EXPECTED COST: - Free tier in MVP. Future Pro tier may charge per-scan or per-month. DATA FRESHNESS: - Scanner version is reported in response.scanner.version. - L1 rule set freshness reflects compuute-scan releases — see github.com/Compuute/compuute-scan/CHANGELOG.md for the latest CVE and threat-intel response timeline. EXAMPLES: Example 1 — scan an MCP server you're evaluating: github_url = "https://github.com/modelcontextprotocol/servers" → score: 0, summary: {critical: 1, high: 94, medium: 22} → top_findings include SSRF, eval, etc. → recommendation: "AVOID — 1 critical and 94 high finding(s)..." Example 2 — scan a clean reference implementation: github_url = "https://github.com/microsoft/azure-devops-mcp" → score: 90+, summary: {critical: 0, high: 1} → recommendation: "REVIEW — 1 high finding(s)..." Example 3 — scan your own dev MCP-server before publishing: github_url = "https://github.com/yourorg/your-mcp" → audit your own surface before others install it OUTPUT FIELDS (stable schema): - repo_url (str): canonical URL of the scanned repo. - score (int): 0-100, higher safer. Coarse summary, not a precision claim. - summary (object): {critical, high, medium, low, info, files_scanned}. - recommendation (str): action guidance derived from severity counts. - findings_count (int): total raw findings (may include false positives). - top_findings (list): up to 10 most severe, each with {id, title, severity, file, line, owasp, cwe}. - l0_discovery (object): MCP transport, tool count, dependency pinning. - performance (object): clone_seconds, scan_seconds, repo_size_bytes. - scanner (object): {name, version, layers_covered}. - _disclaimer (str): MANDATORY triage disclaimer. Read it. Args: github_url: Public GitHub HTTPS URL (e.g. https://github.com/org/repo). Must be public and < 200 MB. v1 is github.com only. Returns: Structured scan result. On error, returns {"error": code, "message": ...} with HTTP-style code (invalid_url, clone_failed, scan_timeout, etc.).
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  • Reverse-lookup a single concept ID (MITRE ATLAS technique like 'AML.T0051', OWASP LLM Top 10 risk like 'LLM01', OWASP Agentic Top 10 issue like 'ASI03', or ISO 42001 Annex A clause like 'A.6') across the AI Defense Matrix. Returns which framework the concept belongs to, the asset rows whose alignment cites it, the cells whose evaluation cellPrompts cite it, and those prompts themselves. Useful when a vendor's product is defined by a specific technique ('we defend AML.T0051') and they need to find which matrix cells to claim. Recognizes only concepts with structured IDs; for prose-only frameworks (NIST IR 8596, CSA AICM, Google SAIF, OWASP AI Exchange) use aidefense_get_framework_alignment instead. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.
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  • Scan a GitHub repository or skill URL for security vulnerabilities. This tool performs static analysis and AI-powered detection to identify: - Hardcoded credentials and API keys - Remote code execution patterns - Data exfiltration attempts - Privilege escalation risks - OWASP LLM Top 10 vulnerabilities Requires a valid X-API-Key header. Cached results (24h) do not consume credits. Args: skill_url: GitHub repository URL (e.g., https://github.com/owner/repo) or raw file URL to scan Returns: ScanResult with security score (0-100), recommendation, and detected issues. Score >= 80 is SAFE, 50-79 is CAUTION, < 50 is DANGEROUS. Example: scan_skill("https://github.com/anthropics/anthropic-sdk-python")
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  • Given a profile of the authorized test target (technology stack, exposed services, authentication type, OS), return a ranked list of ATT&CK techniques and OWASP test cases most relevant to that profile — not a generic dump of all techniques. Ranking factors: platform match, service match, auth type exposure, technique prevalence. Each result includes why it is relevant to this specific profile, the detection opportunity, and the recommended mitigation. Use when starting an authorized engagement to prioritize the testing scope; pair with pentest_guide to get the full methodology for each top-ranked vector.
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  • Get AI Defense Matrix cross-mappings to nine external frameworks: NIST IR 8596, CSA AI Controls Matrix, ISO 42001, Google SAIF, SANS Critical AI Security Guidelines, MITRE ATLAS, OWASP AI Exchange, OWASP LLM Top 10, OWASP Agentic Security Top 10. Each row maps an AI asset class to how that framework applies. Each returned framework also carries a 'concepts' array of the structured IDs (MITRE ATLAS techniques, OWASP risks, ISO clauses) the matrix references for it. Supports a 'buyer' archetype shortcut to scope to the frameworks a particular buyer will care about. Use to translate between framework vocabularies. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.
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  • Load Lenny Zeltser's security assessment report writing context for local analysis. Returns a JSON payload with the risk-adjusted severity model (the spine), reader-first section guidance, completeness criteria, frameworks (NIST SP 800-115/800-30, OWASP WSTG/Risk Rating, CVSS, MITRE ATT&CK, PTES, PCI DSS, CREST), and the mcpHandoffs array. The 'profile' parameter ANNOTATES sections (internal/external applicability) rather than filtering — every section is returned so cross-profile comparisons are possible. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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Matching MCP Servers

Matching MCP Connectors

  • AI-security knowledge as MCP: standards-mapped tools (OWASP, NIST, MITRE) for AI agents.

  • Scan GitHub-hosted AI skills for vulnerabilities: prompt injection, malware, OWASP LLM Top 10.

  • Independently grade an MCP server (A–F) BEFORE connecting an agent to it. Returns spec-conformance, security (OWASP MCP Top 10), reliability, tool-hygiene and transparency sub-scores plus a connect/caution/avoid recommendation. Audited by wmcp.sh; the grade is free and never for sale.
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  • HTTP response headers inspector and security grader. Fetches headers from any public URL and evaluates OWASP-recommended security headers: HSTS, CSP, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, Permissions-Policy. Returns raw headers, per-header security findings, overall grade (A–F), and actionable recommendations. Useful for web app security audits, CDN configuration verification, and compliance checks.
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  • "Top 25 most dangerous software weaknesses" / "OWASP Top 10 CWE mapping" / "CWE view [N]" — fetch a curated CWE view (a published, opinionated selection of weaknesses). View 1003 = simplified mapping (NVD uses this), view 1387 = Top 25, view 1344 = OWASP Top 10. Returns the view's metadata plus the included CWE IDs.
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  • Load Lenny Zeltser's AI Defense Matrix context: the 8-asset x 6-NIST-CSF-2.0-function matrix, nine cross-walked frameworks (NIST IR 8596, CSA AICM, ISO 42001, Google SAIF, SANS Critical AI Security Guidelines, MITRE ATLAS, OWASP AI Exchange, OWASP LLM Top 10, OWASP Agentic Top 10), and the evaluation + cross-mapping playbooks. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.
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