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619,923 tools. Updated 2026-09-28 20:22

"A tool for critical thinking and devil's advocate analysis of AI model plans" matching MCP tools:

  • The merge gate for ANY AI-written output — code, documentation, reports, emails, configs: returns a binary approve/reject verdict with veto power — e.g. it rejects code calling the nonexistent stripe.Inventory API, an N+1 loop with the latency arithmetic to prove it, or a doc claiming success with no evidence. Deterministic reality gates (mock/placeholder veto, gaming and bypass detection, invented-API checks) run first and cannot be overridden; a frontier-model review (ISO/IEC 25010) then scores quality, accuracy, reliability and tech debt. In a benchmark, a frontier model reviewing alone caught reward-gaming and hallucinated APIs 0/6 times in a natural review workflow; these gates catch them deterministically on every call. Read-only: nothing is executed. Call it on every AI-generated deliverable before accepting it; use validate_plan for plans, analyze_code for an advisory report without a verdict.
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  • Get Lenny Zeltser's IR one-page executive brief template. Standalone variant of `ir_get_template` for callers that only want the brief without the long-form report. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's CTI cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `cti_load_context`. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's Vuln one-page executive brief template. Standalone variant of `vuln_get_template` for callers that only want the brief without the long-form report. 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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  • Map the full dependency tree of an npm package and identify CRITICAL supply chain risks at every level. Unlike auditing a flat list of packages, this tool traverses the dependency graph — showing not just your direct dependencies but also what your dependencies depend on. Hidden CRITICAL packages (sole publisher + >10M weekly downloads) often lurk 1-2 levels deep. Risk flags: - CRITICAL: single npm publisher + >10M weekly downloads — sole point of failure for a massive attack surface - HIGH: sole publisher + >1M/wk, OR new package (<1yr) with high adoption - WARN: no release in 12+ months (potential abandonware) depth=1 (default): root package + all direct dependencies depth=2: also traverses one more level for any CRITICAL/HIGH direct deps (reveals hidden exposure) Examples: - audit_dependency_tree("express") — see all of Express's deps and their risk scores - audit_dependency_tree("langchain", 2) — reveal transitive CRITICAL deps 2 levels deep - audit_dependency_tree("@anthropic-ai/sdk") — audit Anthropic SDK full tree Use this when someone asks: - "What am I really depending on?" - "Are my dependencies' dependencies safe?" - "Show me the full supply chain risk for package X"
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  • Get NHTSA 5-Star Safety Ratings for a year/make/model (no VIN required). Answers questions like "is a 2023 RAV4 safe for my family" with: • Overall and crash-test star ratings (when published) • Rollover rating / possibility • NHTSA-evaluated ADAS availability (ESC, FCW, LDW) Ratings are model-year granular from the NHTSA NCAP cache. If no confident rating exists, the tool reports that honestly rather than guessing. For VIN-specific listing details use get_vehicle; for open recalls use check_recalls. CRITICAL: Only use the 'sourceUrl' field from the response for NHTSA links. NEVER invent URLs.
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  • TipRanks AI Stock Analysis — the 0-100 AI score for one or more stocks. Six frontier models (OpenAI, Anthropic, Gemini, xAI, DeepSeek, Perplexity) research each covered stock independently. Every model returns its own 0-100 score, rating (outperform / neutral / underperform), price target, and a weighted factor breakdown across financial performance, technical analysis, valuation, earnings call and corporate events. Use for: "what's the AI score for NVDA", "AI rating on my watchlist", "compare the AI scores of AAPL, MSFT and NVDA", "why do the models disagree on Tesla". Pass every symbol in one call — a multi-ticker call returns one compact row per ticker, which is what a watchlist or ranking question needs. A single ticker also returns every model's score with its factor breakdown plus the bull and bear key points. This is NOT the Smart Score (1-10, eight quantitative factors). It is a separate system, and the two routinely disagree by design. `ai_score` is the headline score and matches the AI Stock Analysis page; `consensus` holds the cross-model average, the high and low scoring models, and the split of rating labels. `upside_pct` is the model's price target against the current price. `as_of` is when the report was generated — reports regenerate on new earnings or a significant price move, so an older date means nothing material has changed since. Coverage is a subset of the stock universe and excludes ETFs. Symbols with no report at all come back under `not_covered`; symbols that are covered but lack a report from the requested `provider` come back separately under `no_report_from_provider`, each listing the models that did score them — so a missing provider is never reported as "this stock has no AI analysis". Args: tickers: Comma-separated tickers (e.g. 'AAPL' or 'AAPL,MSFT'), max 25. provider: Optional single provider to report on. Omit for the headline score that matches the website. detail: 'consensus' (default) or 'full' to add each model's written reasoning. Ignored on multi-ticker calls. Returns JSON: {stocks: [{ticker, company, ai_score, rating, headline_model, price, price_target, upside_pct, as_of, reflects, consensus: {models, avg_score, score_high, score_low, ratings_split, avg_price_target, avg_upside_pct, reports_dated}, providers: [...], key_points: [...]}], not_covered: [...], no_report_from_provider: [{ticker, covered_by}]}. `consensus.reports_dated` appears only when the models did not all run on the same date; `as_of` is always the headline report's own date.
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  • Use this when the user asks what a specific AI tool costs, its pricing model, plans and tiers, whether it has a free tier, hidden costs, or whether the vendor even publishes prices. Returns the recorded pricing model, per-plan prices exactly as the vendor lists them, and — unique to this catalog — documented HIDDEN COSTS: annual-commitment markups, seat minimums, paywalled features, and undisclosed usage fees. Data comes from the RightAIChoice verification engine (8,000+ AI tools re-verified on a rolling weekly cycle). The response distinguishes "the vendor publishes no prices" (their choice — sales call required) from "not recorded in our catalog" (our gap), and never converts currencies. Not for: negotiating quotes, historical price tracking, or financial advice. Prices change frequently — treat the verification date as part of the answer.
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  • Use this when the user asks what a specific AI tool costs, its pricing model, plans and tiers, whether it has a free tier, hidden costs, or whether the vendor even publishes prices. Returns the recorded pricing model, per-plan prices exactly as the vendor lists them, and — unique to this catalog — documented HIDDEN COSTS: annual-commitment markups, seat minimums, paywalled features, and undisclosed usage fees. Data comes from the RightAIChoice verification engine (8,000+ AI tools re-verified on a rolling weekly cycle). The response distinguishes "the vendor publishes no prices" (their choice — sales call required) from "not recorded in our catalog" (our gap), and never converts currencies. Not for: negotiating quotes, historical price tracking, or financial advice. Prices change frequently — treat the verification date as part of the answer.
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  • Logic-trace driver-chain explorer — answers "WHY is this activity critical?" and "WHAT does it drive?". Traces driving predecessors backward from a target activity to project start (the "why critical" chain) and/or driving successors forward to project finish (the "what it drives" chain). Detects constraint-driven artificial criticality and cites AACE RP 49R-06 when found. Supports multiple parallel critical paths (MCPM) and near-critical paths. Use this tool when investigating a single activity's logic chain. For a project-wide CP / logic health audit, use ``critical_path_validator``. Args: xer_path: server-side path to the schedule XER. xer_content: full text of the schedule XER (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. target_activity_codes: list of task_codes to trace; if empty, all CP / near-critical endpoints are traced. direction: 'backward' (predecessors), 'forward' (successors), or 'both' (default). include_near_critical: also trace near-critical endpoints (within float band). output_dir: optional dir for HTML / CSV / JSON outputs. Returns: { "paths": [{chain dicts ...}], "output_files": {dashboard, csv, json}, "project_finish": "YYYY-MM-DD", "project_name": ..., "data_date": ... }
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  • Send a chat completion request to any supported model. Free models are served without payment; the first calls to a PAID model are also free per client (free trials), after which x402 payment (Solana USDC) is required — an unpaid call returns 402 with the exact price; retry with _meta["x402/payment"]. Responses are non-streaming. Pass either mode (auto/eco/premium) or model (explicit id) — one of the two is required; model wins if both are sent. Use this tool to generate text; inspect models and prices first with the free list_models tool. Start with a FREE model (no payment at all) and keep max_tokens >= 200 — a thinking model spends part of that budget on reasoning, so a smaller limit can return an empty answer.
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  • The current AI signal for a region (china, korea, japan, or eu) — recent, relevance-scored items on that region's models, labs, and analysis, ranked by momentum. Includes local-language press translated into English. The canonical regional tool; get_china_signal is a preset of this with region "china". Returns titles, sources, and links.
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  • Get Lenny Zeltser's CTI one-page executive brief template. Standalone variant of `cti_get_template` for callers that only want the brief without the long-form report. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's malware analysis report template. The report covers Executive Summary, Sample Snapshot, Malware Family Identification, Component Inventory, Runtime Requirements, Sources, Capabilities, Indicators of Compromise, Analysis Details, What We Don't Know, optional Infection Vector, optional Detection Engineering, About this Report, Appendix: Analysis Environment, and optional Appendix: Analysis Scripts. 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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  • Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. 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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  • 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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  • AI Music Generator — Generate royalty-free instrumental background tracks from text descriptions. No vocals — perfect for video, podcast, and ad backgrounds. For songs with vocals + lyrics, see AI Song Generator.. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.
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  • AI Melody to Music — Upload a clean single-instrument recording and AI generates instrumental music in your style. No vocals — for songs with vocals, see AI Hum to Song or AI Song Generator.. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.
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  • AI Document Translator — Translate text between 16 languages using AI. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.
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  • Fetch a specific Agentberg skill pack by name. Critical skills (regime, risk_calendar, health) are automatically bundled in get_skills. Optional skills: 'rotation' for sector money-flow analysis, 'narrative' for macro headline synthesis.
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  • List the AI models this CCAPI key can actually call, with their capability category and the MCP tool that drives them. Call this before generating anything if you are unsure a model name is valid — availability depends on the key's group and changes over time. Never guess model names.
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