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205,128 tools. Last updated 2026-06-15 07:24

"Understanding Claude AI's Reasoning and Thinking Ability" matching MCP tools:

  • 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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  • Mutating. Heal an adjacent allied unit. Only units with the heal ability (typically Mages) can use this. healer_id is your healing unit (must be READY or MOVED); target_id is an adjacent allied unit that is damaged. Restores HP based on the healer's magic stat. After healing, the healer's status becomes DONE for this turn. Use get_legal_actions on the healer to see which allies are valid heal targets. Returns the amount healed and the target's updated HP.
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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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  • Transfer ownership of an ENS name to another wallet address. Automatically detects whether the name is wrapped (NameWrapper/ERC-1155) or unwrapped (BaseRegistrar/ERC-721) and builds the correct transaction. Requirements: - The fromAddress must currently own the name - The recipient (toAddress) may be a 0x address or an ENS name — names resolve to their address record - The name must be registered (not expired) WARNING: This transfers FULL ownership. The recipient gains complete control including the ability to transfer, set records, or let the name expire. Resolver records (avatar, addresses, etc.) are NOT affected by transfer — they stay on the name.
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  • Step 1 of the MCP donation flow. Required inputs: campaign_id, amount, and reasoning. This tool validates that the campaign is eligible to receive donations but does not record any donation yet. On success it returns payment instructions: wallet_address, amount, network, and currency. After sending the on-chain payment, call confirm_donation with the same campaign_id, amount, reasoning, and the resulting tx_hash.
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  • STORE reasoning: after solving a problem, store your reasoning trace for future AI. Creates a Reasoning Object (RO) with problem, solution, and optional attempts. Other AI can find this via search_reasoning or resolve_reasoning. Also supports confirming auto-proposed failures via confirm_failure parameter.
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  • Find relevant Smart‑Thinking memories fast. Fetch full entries by ID to get complete context. Spee…

  • AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.

  • 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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  • Estimate token count + USD cost for a text across every major LLM (GPT-4o, GPT-4o-mini, o1, o1-mini, Claude 3.5 Sonnet/Haiku, Claude 3 Opus, Gemini 1.5 Pro/Flash, Llama 3 70B/8B) in one call. Returns per-model: estimated tokens, context-window fit %, input cost, and roundtrip cost (input+output). Also returns the cheapest and costliest model that fits. Use this before sending a long context to decide which model to route to. One call replaces 11 separate tokenizer lookups.
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  • 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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  • Read-only. Return server-tracked match statistics for both teams: total tokens consumed, per-turn thinking time, number of tool calls, and turn count. Available during and after a match. Use this for post-game analysis or mid-game cost monitoring. For game-state history (what moves were made) use get_history instead.
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  • Returns the complete Trident 2D specification including grammar, syntax rules, coordinate system, containers, nodes, connections, shapes, and icon reference. Use this when you need deep understanding of the Trident DSL.
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  • Get monster stats including AC, HP, abilities, skills, senses, and actions. Provide monster index (e.g., "aboleth", "dragon-red-adult", "goblin"). Returns ability scores, skill bonuses, and attack/action details.
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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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  • Call this AFTER the signed-in user has run a skill and reviewed its REAL output (e.g. a computed VAT return), to record their structured quality verdict against the skill and its current version. This is the highest-value feedback the platform collects — especially from accountants, whose verdicts are treated as gold. Use when the user is acting as a REVIEWER grading the AI's output ('that return is wrong', 'the figures came out off', 'rate this', 'here's what the skill got wrong'). This is product-quality QA on the SKILL — NOT a taxpayer handoff (for that, use request_accountant_review) and NOT a generic bug report (that's submit_feedback). Pass the worksheet the skill produced when you have one; the server foot-checks it.
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  • Show which quality dimensions matter for a stated purpose, WITHOUT ranking any models. Returns the inferred weights and the discovery-walk trace. Useful for understanding how XFMS interprets the purpose before committing to a pick.
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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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  • Evaluate lead fit against this business. Provide answers keyed by question keys from get_form_questions. Returns a score (0-100), fit assessment, confidence level, reasoning, and recommended next action. Also accepts legacy lead object for backwards compatibility.
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  • List the team's active investment theses with their descriptions. Useful for an agent that's reasoning about whether a deal fits a thesis.
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  • Save development context (reasoning, decisions, trade-offs) for the current coding session. Use after completing a meaningful unit of work. PREFERRED FORMAT: Wrap content in <context> XML tags: <context> <title>Short title of what was done</title> <agent>your-agent-name (model)</agent> <tags>keyword1, keyword2, keyword3</tags> <story> Organize by phases. Write in first-person engineering journal style. Phase 1 — Title: What user asked, what you did, challenges faced, how you resolved them. Include back-and-forth with the user where it shaped the outcome. </story> <reasoning> Why you chose this approach. <decisions> - Decision — rationale </decisions> <rejected> - Alternative — why rejected </rejected> <tradeoffs> - Trade-off accepted — justification </tradeoffs> </reasoning> <files> path/to/file — new — Description path/to/other — modified — Description </files> <tools>MCPs and resources used</tools> <verification>Test/build results</verification> <risks>Open questions or risks</risks> </context> Required tags: title, story, reasoning. All others (including files) are optional. Context ID, repository, branch, date, and commits are auto-populated. CLI alternative: write content to a file, then run `git why save --file context.md`. Or pipe directly: `echo '<context>...</context>' | git why save`.
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