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444,007 tools. Updated 2026-08-11 15:31

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

  • Pro-tier. Fetch two web pages (your URL and a competitor's) and audit both against the Proximens GEO Engine principles using the same audit engine as audit_url, then compute the delta. INPUT: self_url and competitor_url (both required, http/https). RETURNS: JSON with a 0-100 score per URL (same scoring as audit_url), the principles each page satisfies, the principles each page VIOLATES that the other satisfies (delta_principles), and strategic insights on where to close the gap. USE WHEN you want a competitive GEO gap analysis between your page and a rival's.
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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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  • 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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  • 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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  • Ask a persona to reflect on one of its past recommendations that turned out wrong, and append the reflection to its journal. Server-side pipeline: loads the narada job to recover context + the persona's recommendation, loads the persona's existing journal for continuity, then calls Sonnet in the persona's voice to write a lesson-for-self. Owner-only. Used by /dobranoc after detecting a rollback of a commit tagged [narada:<id>].
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  • Journey facts between two cities (European coverage): fastest and typical duration, whether direct trains run, fewest changes, operators and the guide URL — plus legendary atlas routes on that corridor. Direction-insensitive. Figures are sampled from public schedule data, not live times — treat as planning estimates. An uncovered pair returns an error with a search_routes tip.
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  • CRITICAL STARTING POINT. Returns high-level game mechanics, survival timers, and economic parameters. Use this to understand how to win and avoid penalties.
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  • Structure multi-step debugging and planning across tool calls — not a one-shot think. Tracks hypotheses, observations, plans; detects loops via lastActions; riskLevel high/critical blocks dangerous edits (drop table, prod deploy). Loads projectBrief (stack, key_paths, project_memory recall) on local project. On close, suggestedRemember → call project_memory remember. 4 credits hosted. Hard cap 10 thoughts/session. Call when: stuck after 2+ failed debug attempts, auth/billing/schema change spans 3+ files, flaky test you cannot explain, or you need a plan before editing. Pass lastActions (2–5 recent tool calls), goalAnchor after thought 2, sessionId to resume, area for subsystem. NOT when fix is known, single typo, repeating without new evidence, or session ended (nextThoughtNeeded:false). Read thoughtConfirmed and shouldContinue first. Legacy alias: thinking. Example: keep_thinking({ thought: 'Hypothesis: refresh token not rotated in middleware', thoughtType: 'hypothesis', thoughtNumber: 1, totalThoughts: 5, nextThoughtNeeded: true, confidence: 0.6, goalAnchor: 'Fix auth logout loop', lastActions: ['find_code(query=refreshToken)', 'read_code(target=authMiddleware)'], area: 'auth' }). Read-only.
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  • How to swap $BOBAI on-chain: PancakeSwap V2 router, pair, swap paths, and the critical fee-on-transfer parameters (3% tax, min 15% slippage, SupportingFeeOnTransferTokens methods). $BOBAI reverts on a naive swap — use these.
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  • 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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  • Apply a clamped (±0.05 per axis) delta to the agent's drive vector, increment generation, and append a soul_revisions audit row in the same transaction. Use after a reflection produces a drift signal. Returns the new drive vector and generation.
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  • Launch an autonomous Deep Research session that combines Fodda knowledge graph intelligence with live web research to produce a comprehensive editorial-quality report. The Research Agent plans its own strategy, searches multiple graphs, validates with institutional data, and synthesizes into a narrative brief with inline source citations. Use for complex, multi-faceted questions that need both curated expert intelligence AND current web context — e.g., strategic briefings, market landscape reports, competitive deep dives. Price: $55 (light mode) or $100 (heavy mode). Automatically includes earnings-call intelligence and macro/supplemental data when the topic warrants it (public companies, sectors, economic conditions). You do not need to call the earnings or supplemental tools separately before or after.
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  • The eight thinking-failure problems ContextOverflow covers, phrased the way a human experiences them. Start here to see what exists.
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  • 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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  • US Defense Logistics Agency (DLA) strategic stockpile check for 20 minerals. Returns whether held, quantity tonnes, and DLA stockpile status (active/disposed/not_held). Defense-contractor supply-chain agents use this to gauge DoD mineral security. Full data requires $0.25 USDC via GET /api/stockpile/{commodity} using x402 on Base.
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  • 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.
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  • Analyze a TTAB (Trademark Trial and Appeal Board) proceeding in depth — fetches and analyzes proceeding documents, identifies key arguments, and returns a structured summary with timeline, party positions, and strategic assessment. For authenticated full-mode runs this launches asynchronously (typically 1-2 minutes) and returns a processing handle — then call get_ttab_proceeding_analysis_status to fetch the completed result. Tell the user it is running.
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  • Fetch the full markdown content of a single UploadKit docs page by its path, formatted with title, description, source URL, and the body. When to use: after search_docs identifies a relevant page and you need its full contents to answer a deep question — prefer search_docs first, then get_doc on the top result. Reading the full page avoids relying on snippets that may omit critical context (callbacks, env vars, edge cases). Returns: a plain-text string — "# {title}\n\n> {description}\n\nSource: {url}\n\n---\n\n{content}". If the path is unknown, returns a not-found message suggesting list_docs. Read-only, idempotent.
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  • Deep code review with severity-rated issues, security flags, performance notes, and specific fix suggestions. Returns: critical/high/medium/low issues with exact locations, security vulnerabilities, refactor suggestions, and an overall score. Use when user shares code and says 'review this', 'what's wrong with', 'is this secure', 'how can I improve'.
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