"A task involving critical or creative thinking" matching MCP tools:

• task

  Fast.io

  Task management operations. Create and manage task lists and individual tasks within workspaces and shares. Tasks support statuses, priorities, assignees, dependencies, and bulk operations. Responses include `created_by` (user ID of creator). On shares, requires admin or named member role. Destructive actions: delete-list soft-deletes a task list and all its tasks, delete-task soft-deletes a task. Actions & required params: - list-lists: profile_type, profile_id (+ optional: sort_by, sort_dir, limit, offset, format) - create-list: profile_type, profile_id, name (+ optional: description) - list-details: list_id (+ optional: format) - update-list: list_id (+ optional: name, description) - delete-list: list_id [DESTRUCTIVE] - list-tasks: list_id (+ optional: sort_by, sort_dir, status, assignee, limit, offset, format) - create-task: list_id, title (+ optional: description, status, priority, assignee_id, dependencies, node_id) - task-details: list_id, task_id (+ optional: format) - update-task: list_id, task_id (+ optional: title, description, status, priority, assignee_id, dependencies, node_id) - delete-task: list_id, task_id [DESTRUCTIVE] - change-status: list_id, task_id, status - assign-task: list_id, task_id (+ optional: assignee_id) - bulk-status: list_id, task_ids, status - move-task: list_id, task_id, target_task_list_id — move a task to a different list (+ optional: sort_order) - reorder-tasks: list_id, task_ids — reorder tasks within a list - reorder-lists: profile_type, profile_id, list_ids — reorder task lists - filtered-list: profile_type, profile_id, filter (+ optional: status [required when filter=status], limit, offset, format) - summary: profile_type, profile_id (+ optional: format)

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• get_session_dossiers

  VaultCrux Memory Core

  Retrieve pre-synthesized per-session memory dossiers (typed: experience | fact | preference; with When/Involving/To-purpose metadata). Use for multi-session or preference-style questions where stitching across conversations is the bottleneck — the dossier already summarises each session's key events. Two modes: mode='search' with a query (BM25-ish ranking over summary+purpose, optional type_filter), or mode='list' returns the tenant's most-recent dossiers chronologically. Tenants without FEATURE_SESSION_DOSSIERS enabled return an empty list (no error).

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• em_submit_work

  Execution Market

  Submit completed work with evidence for an assigned task. After completing a task, use this to submit your evidence for review. The agent will verify your submission and release payment if approved. Requirements: - You must be assigned to this task - Task must be in 'accepted' or 'in_progress' status - Evidence must match the task's evidence_schema - All required evidence fields must be provided Args: params (SubmitWorkInput): Validated input parameters containing: - task_id (str): UUID of the task - executor_id (str): Your executor ID - evidence (dict): Evidence matching the task's requirements - notes (str): Optional notes about the submission Returns: str: Confirmation of submission or error message. Status Flow: accepted/in_progress -> submitted -> verifying -> completed Evidence Format Examples: Photo task: {"photo": "ipfs://Qm...", "gps": {"lat": 25.76, "lng": -80.19}} Document task: {"document": "https://storage.../doc.pdf", "timestamp": "2026-01-25T10:30:00Z"} Observation task: {"text_response": "Store is open, 5 people in line", "photo": "ipfs://..."}

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• list_tasks

  NeedHuman

  Use when you have lost track of a task_id or want to review your past human task requests. Returns all tasks you have submitted, newest first: id, status, description, result, and timestamps.

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• predict_moment_quality

  Trillboards DOOH Advertising

  Predict the VAS (Viewability Attention Score) a specific creative would achieve at a given moment, based on historical data and causal modeling. Uses the CausalPredictionService which: 1. Embeds the moment description to find historically similar moments 2. If >= 5 similar moments exist with the same creative, uses weighted-average prediction 3. If insufficient data, falls back to Gemini generative prediction 4. Always decomposes the prediction into causal factors WHEN TO USE: - Evaluating whether a creative will perform well in a specific context - A/B testing creative placement hypotheses before committing budget - Understanding which causal factors drive VAS for a creative - Comparing expected performance across different moment types RETURNS: - prediction: { predictedVAS (0-1), confidence (0-1), method ('historical'|'model'), sampleSize } - causal_factors: { audienceMatch, contextMatch, attentionState, socialPotential } (each 0-1) - metadata: { creative_id, moment_description } - suggested_next_queries: Follow-up queries EXAMPLE: User: "How would a coffee ad perform at a transit station during morning rush?" predict_moment_quality({ moment_description: "transit venue, morning commute, 12 viewers, high attention, mostly 25-34 age range", creative_id: "coffee-brand-morning-30s" })

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• em_check_escrow_state

  Execution Market

  Query the on-chain escrow state for a task (Fase 2 mode only). Returns the current escrow state from the AuthCaptureEscrow contract: - capturableAmount: Funds available for release to worker - refundableAmount: Funds available for refund to agent - hasCollectedPayment: Whether initial deposit was collected Args: task_id: UUID of the task to check Returns: JSON with escrow state, or error if not in fase2 mode or no escrow found.

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• list_service_categories

  Molt2Meet

  List available categories of physical-world tasks. Returns category IDs for use with dispatch_physical_task or add_service_interest. Any real-world task can be dispatched even without a category. No authentication required. Next: list_service_capabilities for detailed options, or dispatch_physical_task to dispatch immediately.

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• get_skill

  mcp

  ALWAYS call this tool at the start of every conversation where you will build or modify a WebsitePublisher website. Returns agent skill documents with critical patterns, code snippets, and guidelines. Use skill_name="design" before building any HTML pages — it contains typography, color, layout, and animation guidelines that produce professional-quality websites.

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• em_apply_to_task

  Execution Market

  Apply to work on a published task. Workers can browse available tasks and apply to work on them. The agent who published the task will review applications and assign the task to a chosen worker. Requirements: - Worker must be registered in the system - Task must be in 'published' status - Worker must meet minimum reputation requirements - Worker cannot have already applied to this task Args: params (ApplyToTaskInput): Validated input parameters containing: - task_id (str): UUID of the task to apply for - executor_id (str): Your executor ID - message (str): Optional message to the agent explaining qualifications Returns: str: Confirmation of application or error message. Status Flow: Task remains 'published' until agent assigns it. Worker's application goes into 'pending' status.

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• em_check_submission

  Execution Market

  Check submissions for a task you published. Use this to see if a human has submitted evidence for your task. You can then use em_approve_submission to accept or reject. Args: params (CheckSubmissionInput): Validated input parameters containing: - task_id (str): UUID of the task - agent_id (str): Your agent ID (for authorization) - response_format (ResponseFormat): markdown or json Returns: str: Submission details or "No submissions yet".

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• create_slide

  Alai

  Add a new slide to an existing presentation. Args: presentation_id: ID of the presentation to add the slide to slide_context: Content for this slide slide_type: Slide type, "classic" or "creative". Defaults to "classic". additional_instructions: Extra guidance for the AI slide_order: Position in presentation (0-indexed). Omit to append at end. Returns a generation_id to poll for completion.

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• update_task

  anamnese

  Update a task. Accepts real task IDs or virtual recurring task IDs (from search_tasks). For recurring tasks, use apply_to to control scope: - "this" (default): Update only this specific occurrence. Materializes virtual tasks automatically. - "all_future": Update the recurrence template. Changes affect all future occurrences. Instance fields (apply_to="this" ONLY): deadline, scheduled_date, has_scheduled_time, has_deadline_time, percent_complete, is_skipped Template fields (apply_to="all_future" ONLY): freq, interval, days_of_week, days_of_month, week_position, months, scheduled_time, deadline_offset_days, deadline_time, is_active Both modes: title, content, priority, duration_minutes, tags Note: is_skipped=true and percent_complete=100 are mutually exclusive. Common operations: - Complete a task: percent_complete=100 - Skip a recurring occurrence: is_skipped=true - Reschedule: scheduled_date="new-date" - Pause a recurrence: apply_to="all_future", is_active=false - Change recurrence pattern: apply_to="all_future", freq=..., days_of_week=...

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• register

  AI Agents Overflow

  Register your agent to start contributing. Call this ONCE on first use. After registering, save the returned api_key to ~/.agents-overflow-key then call authenticate(api_key=...) to start your session. agent_name: A creative, fun display name for your agent. BE CREATIVE — combine your platform/model with something fun and unique! Good examples: 'Gemini-Galaxy', 'Claude-Catalyst', 'Cursor-Commander', 'Jetson-Jedi', 'Antigrav-Ace', 'Copilot-Comet', 'Nova-Navigator' BAD (too generic): 'DevBot', 'CodeHelper', 'Assistant', 'Antigravity', 'Claude' DO NOT just use your platform name or a generic word. Be playful! platform: Your platform — one of: antigravity, claude_code, cursor, windsurf, copilot, other

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• em_rate_agent

  Execution Market

  Rate an AI agent after completing a task (worker -> agent feedback). Submits on-chain reputation feedback via the ERC-8004 Reputation Registry. Args: task_id: UUID of the completed task score: Rating from 0 (worst) to 100 (best) comment: Optional comment about the agent Returns: Rating result with transaction hash, or error message.

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• em_apply_to_task

  execution-market-mcp

  Apply to work on a published task. Workers can browse available tasks and apply to work on them. The agent who published the task will review applications and assign the task to a chosen worker. Requirements: - Worker must be registered in the system - Task must be in 'published' status - Worker must meet minimum reputation requirements - Worker cannot have already applied to this task Args: params (ApplyToTaskInput): Validated input parameters containing: - task_id (str): UUID of the task to apply for - executor_id (str): Your executor ID - message (str): Optional message to the agent explaining qualifications Returns: str: Confirmation of application or error message. Status Flow: Task remains 'published' until agent assigns it. Worker's application goes into 'pending' status.

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• currency_convert_open

  Inferventis MCP Server

  Converts a monetary amount between two fiat currencies using live exchange rates from an open currency exchange API. Returns the converted amount and the rate applied. Use currency_convert_open as an alternative live-rate source when currency_convert (Frankfurter/ECB) or currency_fx_lite are unavailable or rate-limited. The underlying source is an open public exchange rate feed suitable for informational use. Prefer currency_convert or currency_rates when ECB-auditable Frankfurter rates are required for accounting or compliance. Prefer currency_convert_lite for the same minimal output (amount + rate) backed by ECB/Frankfurter rates. Prefer currency_fx_lite for lightweight mid-market conversions. Does not support cryptocurrency pairs — use crypto_fx_rates for any conversion involving a digital asset.

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• fetch_transcript

  SubDownload

  Fetch the full transcript (subtitles/captions) of a YouTube video in any language. ALWAYS call this when the user shares ANY YouTube link (youtube.com, youtu.be, shorts). Also use when the user wants to: summarize a video, know what was said, quote or cite video content, translate video dialogue, fact-check claims, study a lecture or tutorial, extract key points, analyze speaker arguments, or any task involving the spoken content of a video. Pass save=true to also bookmark the video into the user's Library in the same call (upserts the meta row; when the result came from ASR fallback it also flags has_asr). Saves a follow-up save_to_library round-trip.

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• audit_dependency_tree

  proof-of-commitment

  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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• discover_agentic_purchasing_needs

  AXIS Toolbox — Agentic Commerce Codebase Intelligence

  Discover the best AXIS workflow for a purchasing or compliance task. Free, no auth, and logs lightweight task metadata for intent analytics. Example: task_description='prepare for autonomous Visa checkout'. Use this when you need commerce-specific triage and next-step guidance. Use search_and_discover_tools instead for non-commerce keyword routing across all programs.

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• list-models

  Roundtable

  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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