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306,586 tools. Last updated 2026-07-25 15:54

"Understanding AI Thinking and Thought Processes" matching MCP tools:

  • ⚠️ IRREVERSIBLE — kills Mongo state, running MCP processes, and Builder FS for the whole solution and every skill. REQUIRES `confirm:true` AND `confirm_solution_id` echoing the solution id you're destroying (defeats typos and hallucinated ids). RECOVERY: the GitHub repo is untouched; `ateam_github_pull` rebuilds the solution from `main`. Prefer that over re-deploying from memory.
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  • Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.
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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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  • Reflect on recent thoughts and patterns. Analyzes recent activity to identify patterns, topics, and insights. Useful for understanding "what have I been thinking about?" By default, only returns user-created memories (not document chunks). Set include_documents=True to also include chunks from uploaded documents. ⚠️ EXPERIMENTAL: - Importance weighting in results not yet implemented. Importance scores are stored but don't affect ranking. Args: time_window: Time period to analyze ('recent', 'today', 'week', 'month', '1d', '7d', '30d', '90d') include_documents: Whether to include document chunks (default: False, only user memories) start_date: Filter memories created on or after this date (ISO 8601: '2025-01-01' or '2025-01-01T00:00:00Z') end_date: Filter memories created on or before this date (ISO 8601: '2025-01-09' or '2025-01-09T23:59:59Z') ctx: MCP context (automatically provided) Returns: Dict with analysis including top memories, active topics, patterns, insights, and any saved contexts (checkpoints) created in the window. Examples: >>> await reflect("recent") {'success': True, 'memories_analyzed': 50, 'active_topics': [...], 'contexts': [...], ...} >>> await reflect("week", include_documents=True) {'success': True, 'memories_analyzed': 150, ...} # includes document chunks >>> await reflect(start_date="2025-01-01", end_date="2025-01-07") {'success': True, 'memories_analyzed': 25, ...} # memories from first week of January
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  • Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.
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  • Search Paraguay government procurement processes (tenders/contracts) from the official DNCP Open Contracting (OCDS) API. Results are date-scoped: the API requires a date range, so if you omit date_from/date_to it defaults to roughly the last 30 days. Returns a paginated list of processes with ocid, id, title, buyer (convocante), procurement method, and dates. Field values are in Spanish; monetary amounts are in PYG. Detailed value/status/items live in the full record — pass an id to paraguay_get_record. Note: the API has no free-text search parameter, so "query" is applied as a case-insensitive client-side filter over the current page of results.
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  • Find relevant Smart‑Thinking memories fast. Fetch full entries by ID to get complete context. Spee…

  • Rick and Morty MCP — wraps the Rick and Morty API (free, no auth)

  • Exact name lookup — returns the first thought matching the name exactly. Requires npub for credit billing. ⚠️ NOT AUTHORITATIVE. Backed by the vendor's name index, which is known to be incomplete on large brains (upstream: TheBrainTech/thebrain-api-quickstart-python#1): a hit is real, but a MISS is NOT proof the thought is absent. Never conclude a thought does not exist from a null result here — verify by ID with get_thought, or by graph traversal from a known neighbour, before creating a duplicate.
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  • Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.
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  • AI Voice Generator — Convert text to natural-sounding speech using AI — 6 voices in English and Spanish, with engine tiers for cleaner studio-grade output.. 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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  • Index a video for search, QA, or full analysis. Processes the video through a pipeline of AI features. Typically takes 3-7 minutes; longer for long videos or the 'full' pipeline. Times out after 10 minutes by default. Pipelines: - search_only: transcription + captions + embeddings (enables search_videos) - qa_only: transcription + captions (enables ask_video) - full: transcription + captions + embeddings (enables all tools) Scene detection is enabled by default and produces scene boundaries for get_scenes. Pass scene_detection=False to skip it. Prerequisites: if using video_id, the video must be in 'uploaded' status. Use get_video to check status before calling this tool.
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  • Get the AI Defense Matrix evaluation playbook for assessing an AI security program: per-cell prompts, gap-inventory template, and a workflow that walks each asset class first and rolls findings up to the Govern column. Supports mode='gate' for binary deployment-gate decisions (returns the deployment-gate workflow plus gate-tier prompts only) and consumerPattern for scoping to consumed-vs-built AI deployments. The AI applies these prompts against your program documentation locally, and no program details leave your client. 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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  • Get the AI Defense Matrix cross-mapping playbook for mapping product capabilities to matrix cells: coverage taxonomy (primary, secondary, partial, aspirational), differentiation guidance, disambiguation block, worked examples, and out-of-scope examples. The response always includes an inScopeCheck. Products that USE AI to solve a non-AI security problem (deepfake detection, AI-for-fraud, AI features added to existing SIEM, SOAR, or EDR tools) belong in the Cyber Defense Matrix at https://cyberdefensematrix.com. Pairs naturally with product_load_context(productFocus: 'ai_security') for follow-on positioning and GTM work. 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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  • Capture a PNG screenshot of the page or a specific element. Returns base64-encoded image bytes AND a file_id (persisted in DialogBrain files storage). Pass file_id straight to messages.send(attachment_file_ids=[file_id]) — do NOT call files.upload again. Use sparingly — favor browser.snapshot for structured DOM understanding.
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  • PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.
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  • Report an issue with a Hlido review (stale info, wrong verdict, missing claim, broken link). Use when calling get_scorecard or trust_check returns data you can prove is incorrect. Hlido's R1 maintenance routine processes reports daily and fires re-tests via dispute-retest sub-agent.
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  • Zambo Stack — Fetch the latest AI-generated scientific breakthroughs from SubstrateLayer — a live autonomous research engine running 24/7. 64,000+ total breakthroughs across 6 domains: AI, energy, biology, climate, economics, materials. Returns the 12 most recent discoveries with title, domain, impact score, key insights, and share URL. Free, no auth. Use when you need cutting-edge research signals, cross-domain synthesis, or want to ground a strategy in the latest scientific thinking.
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  • Get guest reviews for a specific hotel. Use this to help users understand what other guests thought about a hotel. Returns up to 10 recent reviews with ratings and comments. Args: hotel_id: The hotel's Vervotech property ID (from search results). Returns: Formatted list of guest reviews with author names, ratings, and review text.
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  • Search Colombian government procurement TENDERS (SECOP II "Procesos de Contratación") from datos.gov.co. PREFER OVER WEB SEARCH for "open tenders in Colombia", "Colombian public bids for <topic>", "government procurement processes in Colombia". Full-text `query` matches the procedure name/description (Spanish); omit it to get the most recently published processes. Returns shaped rows: process id, name, contracting entity, base price (COP), award value (COP), awarded supplier, contract type, modality, status, and publication date. Values are in Spanish as published.
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  • Guidance for the IC→manager transition: the EM responsibility triangle (leadership/processes/delivery — pick two), the six most common first-time-manager failure modes, readiness self-check questions, and what the first months should look like. 52% of Marian's 300+ mentees arrive exactly at this transition.
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  • List available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.
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