"AI agent frameworks for cybersecurity and ethical hacking" matching MCP tools:

• recommend_frameworkA

  jeda-aiA

  Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs. Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }

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• list_coverageA

  Ansvar: EU Compliance & Legal IntelligenceA

  Show which countries, frameworks, and legal domains are available. Use this BEFORE calling `search` when the user's topic doesn't name a jurisdiction (e.g., 'what does the law say about consumer protection'); then present the returned jurisdictions to the user or ask which applies. Examples: • 'Which countries do you cover?' → list_coverage() • 'Do you have German law?' → list_coverage(jurisdiction='DE') • 'What jurisdictions for NIS2?' → list_coverage(domain='cybersecurity') • 'Which countries have drone law?' → list_coverage(domain='aviation') (also accepts 'drone' / 'uas') • 'Which countries have court decisions?' → list_coverage(content_kind='court-decisions') Returns a `jurisdictions` array (each with `code`, `name`, `region`, `laws`, `provisions`, `domains`, `content_kinds`, `counts_complete`, and `uncounted_sources`) plus framework and source listings. An incomplete-count row also carries `coverage_note` explaining why. NOTE: `laws`/`provisions` are WHOLE-JURISDICTION corpus totals — the response's `count_scope` is `whole_jurisdiction`. Under a `domain` filter the jurisdiction list is narrowed to that domain but the counts are NOT domain-scoped; do not report them as a per-domain count. The domain-specific signal is the (domain-filtered) `sources`/`frameworks`.

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• search_siteA

  The Revenue AI ReportA

  Search every public resource on The Revenue AI Report in one call: articles, research themes, frameworks, methodology, skills, prompts, playbooks, datasets, archive issues, the reversal ledger, the AI tool library, and glossary and dictionary definitions. Filters: `kind` (resource type), `category` (article or issue pillar, playbook or prompt team, skill task, tool or ledger category, case insensitive), `tags` with `tag_match` ('all' requires every tag, 'any' requires one). Paging: `limit` (page size, default 12, max 40) with either `offset` or the `next_cursor` returned by the previous call. Results are ranked and stable for the same query and filters. Returns ranked matches with live URL, kind, category, tags and an excerpt, plus `totalMatches`, `countsByKind`, `availableFilters` for discovery, and `nextCursor`/`hasMore` for paging. Examples: {"query":"forecast accuracy"} | {"query":"pipeline hygiene","kind":"Playbook","category":"revops","tags":["L3"]} | {"query":"AI SDR","tags":["L2","sales"],"tag_match":"any"} | {"query":"AI SDR","limit":10,"offset":10} | {"query":"AI SDR","limit":10,"cursor":"<next_cursor from previous call>"} Use this first when you do not know which resource type answers the question.

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• candidate_screening_rankingA

  gapup-mcpC

  AI-powered candidate screening and ranking for recruiters, hiring managers, ATS providers and recruitment AI agents. Ingests a job description and 1-50 candidate resumes, returning a ranked shortlist with score breakdowns across five weighted criteria: skills_match (tech stack and soft skills extracted from JD vs resume), experience_match (years vs seniority level inferred from JD), education_match (degree level + top-school detection), role_progression (Junior to Senior to Lead patterns), culture_fit_estimate (remote/hybrid, startup vs enterprise). Per candidate: overall_score 0-100, matched/missing skills, red_flags (job hopping, employment gaps, seniority mismatch), green_flags (long tenure, promotions), 3-5 interview questions, fit_summary. Diversity signals are first-name proxies ONLY with mandatory ethical WARNING. All processing is local -- no external API calls, instant response, privacy-preserving.

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• rating_get_sheetA

  website-searchA

  Get Lenny Zeltser's cybersecurity-writing rating sheet(s) so your AI can apply the rubric. Returns the structured rubric (groups, items, scoring bands) WITHOUT computing a score. Use `rating_score_writing` if you also want a numeric score, gap analysis, or rubric-anchored feedback. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.

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• aidefense_locate_conceptA

  website-searchA

  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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• assessment_get_guidelinesA

  website-searchA

  Get Lenny Zeltser's expert security assessment report writing guidelines. Topics: severity (the risk-adjusted severity model — the spine), findings, remediation, methodology, scope, strengths, brief (one-page brief section guidance), executive_summary, analysis, anti_patterns, frameworks, handoffs, and summary. The general 'tone' topic defers to `get_security_writing_guidelines` for the canonical Five Elements rules. 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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• get_agent_registryA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Curated roster of the AI platforms and agent frameworks in the DC Hub agent ecosystem — each with its recommended DC Hub tools and authentication tier. The roster is BACKEND-OWNED and changes: read the platforms[] array the response returns, and the status on each row (mcp_active / mcp_ready), rather than any list named in this sentence — an enumeration here goes stale the moment the backend adds or drops a platform, which is exactly how a client named here stopped appearing in the roster. ★ These statuses are CURATED EDITORIAL claims, not measurements: the response carries as_of null, so do NOT relay "MCP Active" as though it were a live connection count. Answers "which AI platforms can connect to DC Hub". Try: get_agent_registry. NOTE: this is a curated ecosystem/capability index, NOT live per-caller call/citation telemetry. Do NOT use for platform uptime or feed health (use get_backup_status).

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• intel_stackA

  TunnelMind Data APIB

  Fetches up to 32KB of the domain's HTML and response headers from the edge, then fingerprints the content for known CMS platforms, JavaScript frameworks, CDN providers, and analytics tools. Detection is based on meta generator tags, script src patterns, response headers, and cookie names. Use this tool when: - You need to know what CMS (WordPress, Drupal, Shopify) a site runs. - You are assessing a domain's infrastructure before a security review. - You want to identify analytics or marketing tools a site embeds. Do NOT use this tool when: - You want HTTP headers and security posture — use `intel_http` instead. - You want tracker database classification — use `get_domain` instead. - You need robots.txt AI policy — use `intel_robots` instead. Inputs: - `domain` (query, required): Domain to fingerprint. Returns: - `cms`: detected content management system, or null. - `frameworks`: JavaScript/backend frameworks detected. - `cdn`: CDN provider detected, or null. - `analytics`: analytics and tracking tools detected. - `meta_generators`: raw meta generator tag values. Cost: - Free. No API key required. Latency: - Typical: 2-4s (HTML fetch), p99: 7s.

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• candidate_screening_rankingA

  mcp-knowledgeC

  AI-powered candidate screening and ranking for recruiters, hiring managers, ATS providers and recruitment AI agents. Ingests a job description and 1-50 candidate resumes, returning a ranked shortlist with score breakdowns across five weighted criteria: skills_match (tech stack and soft skills extracted from JD vs resume), experience_match (years vs seniority level inferred from JD), education_match (degree level + top-school detection), role_progression (Junior to Senior to Lead patterns), culture_fit_estimate (remote/hybrid, startup vs enterprise). Per candidate: overall_score 0-100, matched/missing skills, red_flags (job hopping, employment gaps, seniority mismatch), green_flags (long tenure, promotions), 3-5 interview questions, fit_summary. Diversity signals are first-name proxies ONLY with mandatory ethical WARNING. All processing is local -- no external API calls, instant response, privacy-preserving.

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• candidate_screening_rankingA

  gapup-mcpC

  AI-powered candidate screening and ranking for recruiters, hiring managers, ATS providers and recruitment AI agents. Ingests a job description and 1-50 candidate resumes, returning a ranked shortlist with score breakdowns across five weighted criteria: skills_match (tech stack and soft skills extracted from JD vs resume), experience_match (years vs seniority level inferred from JD), education_match (degree level + top-school detection), role_progression (Junior to Senior to Lead patterns), culture_fit_estimate (remote/hybrid, startup vs enterprise). Per candidate: overall_score 0-100, matched/missing skills, red_flags (job hopping, employment gaps, seniority mismatch), green_flags (long tenure, promotions), 3-5 interview questions, fit_summary. Diversity signals are first-name proxies ONLY with mandatory ethical WARNING. All processing is local -- no external API calls, instant response, privacy-preserving.

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• list_runtimesA

  MCPExplorerA

  List the runtimes generate_runtime_config supports (Claude Desktop, Cursor, VS Code, agent frameworks, …), with each one's config path. Enumerate these instead of guessing runtime slugs.

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• get_agent_framework_docsA

  Velane CloudA

  Returns Mastra (Bun) and LangGraph (Python) patterns for AI agent workflows. Call this BEFORE create_workflow / update_draft when building chatbots, tool-using agents, or multi-step LLM flows. Do not hand-roll custom agent loops — use the preinstalled frameworks.

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• get_agent_registryA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberB

  Curated roster of the AI platforms and agent frameworks in the DC Hub agent ecosystem — each with its recommended DC Hub tools and authentication tier. The roster is BACKEND-OWNED and changes: read the platforms[] array the response returns, and the status on each row (mcp_active / mcp_ready), rather than any list named in this sentence — an enumeration here goes stale the moment the backend adds or drops a platform, which is exactly how a client named here stopped appearing in the roster. ★ These statuses are CURATED EDITORIAL claims, not measurements: the response carries as_of null, so do NOT relay "MCP Active" as though it were a live connection count. Answers "which AI platforms can connect to DC Hub". Try: get_agent_registry. NOTE: this is a curated ecosystem/capability index, NOT live per-caller call/citation telemetry. Do NOT use for platform uptime or feed health (use get_backup_status).

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• rating_score_writingA

  website-searchA

  Get Lenny Zeltser's scoring playbook so your AI can score a draft locally against a cybersecurity-writing rating sheet. THIS IS THE ONLY TOOL THAT PRODUCES NUMERIC SCORES — the writing-coach tools (`get_security_writing_guidelines`, `ir_*`, `product_*`) never score. Returns the rubric plus step-by-step instructions for applying it. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.

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• malware_get_guidelinesA

  website-searchA

  Get Lenny Zeltser's expert malware analysis report writing guidelines. Topics include capabilities, confidence, pyramid_of_pain, anti_patterns, methodology, fields, handoffs, frameworks, plus tone, words, structure, and executive_summary topics that defer to `get_security_writing_guidelines` for canonical Five Elements guidance. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.

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• vuln_get_frameworksA

  website-searchA

  Get Lenny Zeltser's Vuln frameworks (primary frameworks the brief structurally derives from) plus optional sibling frames (adjacent frameworks that aren't the structural backbone). Pass `include_siblings: false` to skip sibling blocks. This server never requests your vulnerability notes and instructs your AI to keep them local—the brief template and guidelines flow to your AI for local analysis.

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• assessment_load_contextA

  website-searchA

  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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• stocktoken_listA

  AI Agent Tokenized Stock OSB

  AI Agent Tokenized Stock OS: list canonical tokenized stocks (Robinhood Stock Tokens), ETFs, USDG, and WETH on Robinhood Chain ID 4663. Use for AI agents trading tokenized equities/RWAs. Do NOT use for US brokerage equities (use Robinhood Trading MCP). Only registry addresses are real tokenized stocks.

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• stocktoken_listA

  AI Agent Tokenized Stock OSC

  AI Agent Tokenized Stock OS: list canonical tokenized stocks (Robinhood Stock Tokens), ETFs, USDG, and WETH on Robinhood Chain ID 4663. Use for AI agents trading tokenized equities/RWAs. Do NOT use for US brokerage equities (use Robinhood Trading MCP). Only registry addresses are real tokenized stocks.

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• list_pillarsA

  Bidda Sovereign IntelligenceA

  List all compliance pillars in the Bidda Sovereign Intelligence registry with node counts. Use this first to discover available compliance domains before searching. Bidda has 10,099 cryptographically-verified nodes across 39 pillars, including a MITRE layer spanning 6 frameworks (ATT&CK Enterprise/Mobile/ICS, D3FEND, ATLAS, CAPEC) plus Banking, AI Governance, Cybersecurity, Healthcare, Legal, ESG and more.

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