"Resources on Deep Thinking, Critical Reflection, and Strategic Planning" matching MCP tools:

• audit_dependency_treeA

  Commit — Supply Chain Risk ScoringA

  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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• hemelion_choose_reflection_modeA

  Hemelion Decision and Pattern ReflectionA

  Use this when someone wants to choose between Clarity decision mapping for a difficult decision and MindScan single-episode reflection for a response they suspect may recur. Returns a no-charge reflection mode, a clarifying question, or a safety boundary. Do not use for diagnosis, emergencies, or legal, medical, financial, hiring, or insurance decisions.

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• plan_createA

  PlanExeA

  Call only after example_prompts and after you have completed prompt drafting/approval (non-tool step). PlanExe turns the approved prompt into a strategic project-plan draft (20+ sections) in ~10-20 min. Sections include: executive summary, interactive Gantt charts, investor pitch, project plan with SMART criteria, strategic decision analysis, scenario comparison, assumptions with expert review, governance structure, SWOT analysis, team role profiles, simulated expert criticism, work breakdown structure, plan review (critical issues, KPIs, financial strategy, automation opportunities), Q&A, premortem with failure scenarios, self-audit checklist, and adversarial premise attacks that argue against the project. The adversarial sections (premortem, self-audit, premise attacks) surface risks and questions the prompter may not have considered. Returns plan_id (UUID); use it for plan_status, plan_stop, plan_retry, and plan_file_info. To track progress, poll plan_status at reasonable intervals (e.g. every 5 minutes). Optionally, run `curl -N <sse_url>` in a background shell as a completion detector — the stream auto-closes on terminal state (completed/failed/stopped). If you lose a plan_id, call plan_list to recover it. If the same prompt + model_profile is submitted by the same user within a short window, the existing plan is returned (with deduplicated=true) instead of creating a new one. If you are unsure which model_profile to choose, call model_profiles first. If your deployment uses credits, include user_api_key to charge the correct account. Common error codes: INVALID_USER_API_KEY, USER_API_KEY_REQUIRED, INSUFFICIENT_CREDITS.

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• create_booking_intentA

  travelA

  Create a booking intent — returns a deep-link the user clicks to complete the booking on autonomad.ai. Signing up is free, with no trial and no subscription. ALWAYS call this instead of trying to book directly through MCP — bookings require payment + identity verification that must happen on the web. WHEN TO CALL — generate a deep-link ONLY after the user has picked something concrete: a specific flight, a specific hotel, or both (a trip). Do NOT call this for browsing or for activities/events alone. Activities and events are picked on the autonomad.ai add-ons page AFTER the user lands via the deep-link — Claude should describe them but not generate per-activity/per-event intents. INTENT TYPE GUIDE — pick exactly one: - 'flight' → user picked a flight only. offer_data = the flight offer object verbatim from search_flights, PLUS a top-level `passengers: <number>` field (the number of travelers the user originally requested — search_flights individual offers don't echo this back, so you must add it explicitly). - 'hotel' → user picked a hotel only. offer_data = the hotel offer from search_hotels PLUS top-level `check_in` and `check_out` (YYYY-MM-DD) as STRINGS. CRITICAL: search_hotels does NOT echo dates back inside the offer object — you MUST add them yourself (use the same dates you passed to search_hotels) or the booking page will fall back to an empty form and the user will have to re-enter everything. Also include `adults: <number>` and `rooms: <number>`. - 'trip' → user picked BOTH a flight AND a hotel together for the same trip. Pack them in offer_data as { flight: { ...offer, passengers: <n> }, hotel: { ...offer, adults: <n>, rooms: <n>, check_in, check_out } }. ONE deep-link covers both. Don't generate two separate intents (flight + hotel) for the same trip — that produces two deep-links and a confusing user experience. For activities, events, and experience browsing: describe what's available in your reply, but do NOT call create_booking_intent. Tell the user they'll pick those on autonomad.ai's add-ons page after they click the deep-link for their flight/hotel. USER-FACING REPLY REQUIREMENTS — every time you create a booking intent, your reply text MUST include: 1. The deep_link as a clickable markdown link, e.g. '[Complete on autonomad.ai →](<deep_link>)' or 'Open: <deep_link>'. 2. That the account is free. Autonomad costs the traveller nothing — no subscription, no trial, no card required to sign up; they pay only for the travel they book. NEVER describe a trial, a free month, or Premium: none of those exist, and promising one is a claim we cannot honour. 3. The link expiry window (e.g. '~30 minutes — say the word and I'll regenerate if it lapses.'). CRITICAL: always echo the original passenger / adults / travelers count into offer_data. Without it the booking page defaults to 2 travelers regardless of what the user asked for.

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• review_dayA

  reassignA

  Confirm or discard a whole day's reflection (by `date`, ISO YYYY-MM-DD), once you've marked its events with write_events' `reflect` op. `action:"confirm"` marks the day reviewed — "this is how it went" — freezing a per-day adherence snapshot (how closely actuals matched the plan, with per-area/per-type breakdowns), which get_schedule then returns as that day's `review` block and which the stats use; re-confirming refreshes it. `action:"discard"` fully resets the day: it removes the review, clears the kept/skipped/changed marks + actual times off the day's planned events (returned as `updated`), and deletes events ADDED only as part of the reflection (`deletedIds`) — use it to start a day's reflection over or drop one confirmed by mistake. Only a past day can be confirmed; discard works on any day, also on today's check-offs. A recorded undo receipt includes `undoToken` + `expiresAt` (UTC); call undo_changes before it expires. An empty discard returns `noop:true`, empty result arrays, and no undo receipt.

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  Destructive

  OAuth

• generate_business_modelA

  TrendIdeaA

  Generate a Business Model Canvas (Osterwalder) for an idea with strategic depth: key partners, activities, resources, value propositions, customer relationships, channels, segments, cost structure, revenue streams and a synthesis. Returns cached canvas instantly if one exists, otherwise generates fresh analysis. Spends 1 credit only when generating new content. Not read-only; pass an ideaId you own.

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• nsw_da_get_applicationA

  NSW Development Applications (x402)A

  Get the full record of one of the NSW development applications by its Planning Portal application number (PAN). Returns every location with lot and plan, all development types, submission, lodgement, determination and exhibition dates, cost, dwellings, storeys, lots and the remaining source fields. Take PAN numbers from search_applications or search_nearby_applications. An unknown number returns not_found and is not charged. Costs $0.005 per call, paid via x402 in USDC on Base; clients without x402 support receive the payment requirements as an error result. Failed calls are not charged. Source: NSW Department of Planning, Housing and Infrastructure (NSW Planning Portal), CC BY 4.0.

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• nsw_cdc_get_applicationA

  NSW Development Applications (x402)A

  Get the full record of one of the NSW complying development certificates (CDCs) by its Planning Portal CDC number. Returns every location with lot and plan, all development types, submission, lodgement, determination and exhibition dates, cost, dwellings, storeys, lots and the remaining source fields. Take CDC numbers from search_applications or search_nearby_applications. An unknown number returns not_found and is not charged. Costs $0.005 per call, paid via x402 in USDC on Base; clients without x402 support receive the payment requirements as an error result. Failed calls are not charged. Source: NSW Department of Planning, Housing and Infrastructure (NSW Planning Portal), CC BY 4.0.

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• get_fomc_rate_probabilityA

  Stratalize FinanceB

  Use when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session. Returns illustrative cut, hike, and hold probabilities for the next three FOMC meetings based on current FRED fed funds data. Scenario planning tool — not futures-implied market odds. Example: Hold probability 68% at next meeting, cut probability 31% — conditioned on fed funds at 5.33% and latest CPI print. Source: FRED St. Louis Fed. $0.02 USDC per call.

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• proximens_geo_audit_urlA

  Proximens OracleA

  Pro-tier. Fetch and analyze a web page, then audit it against the Proximens GEO Engine principles across all major GEO dimensions (structured data, crawler access, content depth, freshness, E-E-A-T, multimodal). INPUT: url (required, http/https); optional mode ("fast" = quick signal checks, returns in seconds — the default; "deep" = a full AI-synthesized consultancy report in Dutch with a 7-dimension scorecard and sector benchmark, takes ~30-50s), client_name (report header), branche_hint ("main:sub", e.g. "health_wellness:yoga_studio"), max_issues (1-25, default 10). RETURNS: JSON with a 0-100 score, severity-ranked issues (critical/major/minor) each with a finding and an actionable suggestion, top recommendations, and a markdown report; deep mode additionally returns score_set (7 GEO dimensions), sector (benchmark cohort), and a full consultancy-grade report_markdown (deep_mode="timeout_fallback" means the synthesis exceeded its budget and the fast result was returned instead). USE fast mode for quick checks and bulk triage; USE deep mode when you need a client-ready audit report. Free tier is blocked.

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• validate_best_practicesA

  ALM X++ MCP ServerA

  Run an in-house best-practice audit on any AOT object (custom or standard), from the indexed source -- NOT the Microsoft BP checker. Rules are this server's own: SEC (security chain), PERF (firstOnly, set-based, N+1), TXN (ttsbegin/ttscommit pairing), ERR (error handling), COC (next() vs super()), QUAL, DATA (EDT on fields), CONV (naming, ISV prefix) and CLOUD. It runs pre-compile and needs no D365 install, so it catches things while the code is being written -- but it does not replace xppbp.exe, whose rule set and monikers are different. For the authoritative Microsoft verdict run run_best_practices_check (xppc.exe -BestPractices, whole model) or run_best_practices_check_scoped (xppbp.exe, one object). Returns violation table: severity (Critical/Warning), rule ID, code snippet, fix instruction. For deep N+1 / row-by-row performance profiling use detect_performance_issues instead. [!] Auto-fixing Critical violations requires D365_CUSTOM_MODEL_PATH (custom code only).

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• query_auditA

  XMemoA

  Advanced audit query. source='events' queries action/time/target audit events (for normal users prefer memory_activity); source='consolidation' inspects consolidation/lifecycle/reflection audit records (admin). Consolidates the legacy query_audit_events/query_consolidation_audit tools.

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• get_fomc_rate_probabilityA

  Stratalize GovernanceB

  Use when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session. Returns illustrative cut, hike, and hold probabilities for the next three FOMC meetings based on current FRED fed funds data. Scenario planning tool — not futures-implied market odds. Example: Hold probability 68% at next meeting, cut probability 31% — conditioned on fed funds at 5.33% and latest CPI print. Source: FRED St. Louis Fed. $0.02 USDC per call.

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• get_fomc_rate_probabilityA

  Stratalize HealthcareB

  Use when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session. Returns illustrative cut, hike, and hold probabilities for the next three FOMC meetings based on current FRED fed funds data. Scenario planning tool — not futures-implied market odds. Example: Hold probability 68% at next meeting, cut probability 31% — conditioned on fed funds at 5.33% and latest CPI print. Source: FRED St. Louis Fed. $0.02 USDC per call.

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• get_fomc_rate_probabilityA

  Stratalize Real EstateB

  Use when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session. Returns illustrative cut, hike, and hold probabilities for the next three FOMC meetings based on current FRED fed funds data. Scenario planning tool — not futures-implied market odds. Example: Hold probability 68% at next meeting, cut probability 31% — conditioned on fed funds at 5.33% and latest CPI print. Source: FRED St. Louis Fed. $0.02 USDC per call.

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• get_fomc_rate_probabilityA

  Stratalize Crypto & DeFiB

  Use when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session. Returns illustrative cut, hike, and hold probabilities for the next three FOMC meetings based on current FRED fed funds data. Scenario planning tool — not futures-implied market odds. Example: Hold probability 68% at next meeting, cut probability 31% — conditioned on fed funds at 5.33% and latest CPI print. Source: FRED St. Louis Fed. $0.02 USDC per call.

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• get_fomc_rate_probabilityA

  Stratalize IntelligenceB

  Use when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session. Returns illustrative cut, hike, and hold probabilities for the next three FOMC meetings based on current FRED fed funds data. Scenario planning tool — not futures-implied market odds. Example: Hold probability 68% at next meeting, cut probability 31% — conditioned on fed funds at 5.33% and latest CPI print. Source: FRED St. Louis Fed. $0.02 USDC per call.

  ConnectorNo auth

• get_fomc_rate_probabilityA

  Stratalize OracleB

  Use when providing monetary policy narrative context for a macro brief, investment committee, or CFO rate planning session. Returns illustrative cut, hike, and hold probabilities for the next three FOMC meetings based on current FRED fed funds data. Scenario planning tool — not futures-implied market odds. Example: Hold probability 68% at next meeting, cut probability 31% — conditioned on fed funds at 5.33% and latest CPI print. Source: FRED St. Louis Fed. $0.02 USDC per call.

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• planProjectWorkToolA

  1lev1A

  Turn a free-text brief into a planning board for a project: proposed missions, chores (acts) and resources, deduplicated against what the project already has. Nothing is created - the tool returns a reviewUrl where the user approves each row in the real form. Use this when the user describes something they want the project to achieve in loose words. If the conversation already holds a concrete breakdown (tasks, scope, decisions), use createPlanBoardTool instead - it saves your rows as written. Requires a projectId (use findUserProjectsTool first if unknown). On failure, `error.retryable` says whether calling again can help. Docs: https://1lev1.com/mcp#planProjectWorkTool

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• abstraction-qualityA

  Clairvoyance

  Evaluates whether abstractions provide a genuinely different way of thinking or are structurally shallow. Use when adjacent layers feel redundant, wrappers add boilerplate without depth, or an abstraction feels leaky. Not for a single module's interface-to-implementation ratio (use deep-modules) or information leakage across boundaries (use information-hiding).

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• list_categoriesA

  raumnebenanA

  Returns a list of all available product knowledge categories, each with a short description. Categories represent the main pillars of Product Thinking – Foundation, Sense, Focus, Discovery, and Delivery. Each category provides structured resources for product owners, designers, and teams, covering groundwork, user research, opportunity analysis, validation, and agile delivery. Use this tool to guide users to the right area for their current product challenge.

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