"A server for orchestrating intelligent agents to solve spatial reasoning tasks in 3D environments" matching MCP tools:

• em_batch_create_tasks

  execution-market-mcp

  Create multiple tasks in a single operation with escrow calculation. ⚠️ **WARNING**: This tool BYPASSES the standard payment flow by calling db.create_task() directly instead of using the REST API (POST /api/v1/tasks). This means it skips x402 payment verification and balance checks. For production use, tasks should be created via the REST API to ensure proper payment authorization and escrow handling. Supports two operation modes: - ALL_OR_NONE: Atomic creation (all tasks or none) - BEST_EFFORT: Create as many as possible Process: 1. Validates all tasks in batch 2. Calculates total escrow required 3. Creates tasks (atomic or best-effort) - **BYPASSING PAYMENT FLOW** 4. Returns summary with all task IDs Args: params (BatchCreateTasksInput): Validated input parameters containing: - agent_id (str): Your agent identifier - tasks (List[BatchTaskDefinition]): List of tasks (max 50) - payment_token (str): Payment token (default: USDC) - operation_mode (BatchOperationMode): all_or_none or best_effort - escrow_wallet (str): Optional custom escrow wallet Returns: str: Summary of created tasks with IDs and escrow details.

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• em_batch_create_tasks

  Execution Market

  Create multiple tasks in a single operation with escrow calculation. ⚠️ **WARNING**: This tool BYPASSES the standard payment flow by calling db.create_task() directly instead of using the REST API (POST /api/v1/tasks). This means it skips x402 payment verification and balance checks. For production use, tasks should be created via the REST API to ensure proper payment authorization and escrow handling. Supports two operation modes: - ALL_OR_NONE: Atomic creation (all tasks or none) - BEST_EFFORT: Create as many as possible Process: 1. Validates all tasks in batch 2. Calculates total escrow required 3. Creates tasks (atomic or best-effort) - **BYPASSING PAYMENT FLOW** 4. Returns summary with all task IDs Args: params (BatchCreateTasksInput): Validated input parameters containing: - agent_id (str): Your agent identifier - tasks (List[BatchTaskDefinition]): List of tasks (max 50) - payment_token (str): Payment token (default: USDC) - operation_mode (BatchOperationMode): all_or_none or best_effort - escrow_wallet (str): Optional custom escrow wallet Returns: str: Summary of created tasks with IDs and escrow details.

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• switch_dannet_server

  dannet

  Switch between local and remote DanNet servers on the fly. This tool allows you to change the DanNet server endpoint during runtime without restarting the MCP server. Useful for switching between development (local) and production (remote) servers. Args: server: Server to switch to. Options: - "local": Use localhost:3456 (development server) - "remote": Use wordnet.dk (production server) - Custom URL: Any valid URL starting with http:// or https:// Returns: Dict with status information: - status: "success" or "error" - message: Description of the operation - previous_url: The URL that was previously active - current_url: The URL that is now active Example: # Switch to local development server result = switch_dannet_server("local") # Switch to production server result = switch_dannet_server("remote") # Switch to custom server result = switch_dannet_server("https://my-custom-dannet.example.com")

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• em_get_tasks

  Execution Market

  Get tasks from the Execution Market system with optional filters. Use this to monitor your published tasks or browse available tasks. Args: params (GetTasksInput): Validated input parameters containing: - agent_id (str): Filter by agent ID (your tasks only) - status (TaskStatus): Filter by status (published, accepted, completed, etc.) - category (TaskCategory): Filter by category - limit (int): Max results (1-100, default 20) - offset (int): Pagination offset (default 0) - response_format (ResponseFormat): markdown or json Returns: str: List of tasks in requested format. Examples: - Get my published tasks: agent_id="0x...", status="published" - Get all completed tasks: status="completed" - Browse physical tasks: category="physical_presence"

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• ask

  bstorms.ai — Free Execution Playbooks + Agent Brainstorming

  Post a question to the network. Other agents can answer and earn USDC. Broadcast (default): question visible to all agents via browse_qa(). Directed: pass agent_id + playbook_id to send privately to one agent — only they see it in their inbox, never in broadcast browse_qa(). playbook_id is required when agent_id is set. Args: api_key: Your API key question: Your question (max 2000 chars) tags: Comma-separated tags for discoverability (broadcast only) agent_id: Optional — direct this question to a specific agent playbook_id: Required when agent_id is set — playbook the question is about

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• propose_mailbox_md_edit

  mailbox

  Propose changes to the renter's MAILBOX.md instructions with reasoning. The renter will see your suggestion in their dashboard and can accept, reject, or modify it. Use this when you observe patterns that could be codified into standing instructions.

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• licium_discover

  Licium

  Browse and compare Licium's agents and tools. Use this when you want to SEE what's available before executing. WHAT YOU CAN DO: - Search tools: "email sending MCP servers" → finds matching tools with reputation scores - Search agents: "FDA analysis agents" → finds specialist agents with success rates - Compare: "agents for code review" → ranked by reputation, shows pricing - Check status: "is resend-mcp working?" → health check on specific tool/agent - Find alternatives: "alternatives to X that failed" → backup options WHEN TO USE: When you want to browse, compare, or check before executing. If you just want results, use licium instead.

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• export_asset

  Web3d-mcp

  Package generated 3D scene output into downloadable files. Formats: r3f -> Packages R3F code into a named .tsx file. Requires r3f_code string from generate_r3f_code. Does NOT regenerate code - it packages what you give it. json -> Packages scene_data into a named .json file. Requires scene_data object from generate_scene. Call order: For .tsx file: generate_r3f_code(scene_data) -> export_asset({ r3f_code, format: "r3f" }) For .json file: generate_scene(scene_plan) -> export_asset({ scene_data, format: "json" }) For visual preview of the scene layout, use the preview tool instead. preview tool returns SVG wireframe + spatial validation. export_asset does not generate previews. Do NOT pass synthesized_components to export_asset. Pass them to generate_r3f_code, then pass the resulting r3f_code here.

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• hivelearn_get_course_structure

  HiveLearn

  Return a compact titles-only tree of the course: course → modules → lessons. Ideal for agents to plan reorders, spot empty lessons, or summarize a course. Does NOT include lesson body content.

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• get_user_profile

  anamnese

  PRIMARY TOOL - Call this at the START of every conversation to load comprehensive user context. Returns: - current_datetime: Current date and time in the user's timezone (ISO 8601 with offset) - All active facts about the user (preferences, personal info, relationships) - tasks_overdue: Tasks with scheduled_date OR deadline in the past - tasks_today: Tasks scheduled OR due today (time >= now), plus unscheduled tasks (no date set) - tasks_tomorrow: Tasks scheduled OR due tomorrow (includes projected recurring tasks) - Active goals - Recent moments from the last 5 days - Latest 15 user-facing notes (id + description). Use get_note to retrieve full content. - ai_memory: Latest 15 AI memory notes from your previous sessions (id + description). Use get_note to retrieve full content. SELF-LEARNING: Review the ai_memory array — these are notes you saved in previous sessions about how to best assist this user. Load relevant ones with get_note. Throughout the conversation, save new learnings anytime via save_note with scope="ai_client" whenever you discover something worth remembering. - tasks_recently_completed: Tasks completed or skipped in the last 7 days Each task includes: - category_reason: 'scheduled' | 'deadline' | 'both' - explains why it's in that array - has_scheduled_time: true if task has a specific scheduled time, false if all-day - has_deadline_time: true if deadline has a specific time, false if all-day Task placement uses scheduled_date when present, otherwise deadline. Each task appears in exactly one category. For calendar events, the user should connect a calendar MCP (Google Calendar MCP, Outlook MCP) in their AI client. Query those MCPs alongside Anamnese for a complete daily view. This provides essential grounding for personalized, context-aware conversations.

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• get_board

  cnvs.app

  Full structured JSON state of a board: texts (id, x, y, content, color, width, postit, author), strokes (id, points, color, author), images (id, x, y, width, height, dataUrl, thumbDataUrl, author; heavy base64 >8 kB elided to dataUrl:null, tiny images inlined). Use this for EXACT ids/coordinates/content (needed for `move`, `erase`, editing a text by id). For visual layout (where is empty space? what overlaps?) call `get_preview` instead — it's much cheaper for spatial reasoning than a huge JSON dump.

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• licium_discover

  Licium

  Browse and compare Licium's agents and tools. Use this when you want to SEE what's available before executing. WHAT YOU CAN DO: - Search tools: "email sending MCP servers" → finds matching tools with reputation scores - Search agents: "FDA analysis agents" → finds specialist agents with success rates - Compare: "agents for code review" → ranked by reputation, shows pricing - Check status: "is resend-mcp working?" → health check on specific tool/agent - Find alternatives: "alternatives to X that failed" → backup options WHEN TO USE: When you want to browse, compare, or check before executing. If you just want results, use licium instead.

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• search_agent_directory

  Name Whisper — ENS Intelligence Layer

  Search the AI agent directory — find registered agents by name, capability, protocol support, or reputation. Powered by the live ERC-8004 registry via 8004scan (110,000+ agents indexed across 50+ chains). Returns agent identity, owner wallet/ENS, reputation scores, supported protocols (MCP/A2A/OASF), verification status, and links to 8004scan profiles. Examples: - "trading agents on Base" → search for trading agents filtered to Base chain - "MCP agents" → find agents that support the Model Context Protocol - "high reputation agents" → set minReputation to find top-scored agents

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• register_agent

  Molt2Meet

  Register to dispatch physical-world tasks. No existing account needed. Returns an API key (m2m_...) required for all subsequent tools — store it securely, shown only once. For OpenClaw agents: provide agentFramework='openclaw', your callbackUrl (e.g. http://host:port/hooks), and callbackSecret (your hooks.token). Molt2Meet will then push task status events directly to you via /hooks/wake or /hooks/agent. Before registering, call get_legal_documents to read the terms you are accepting. Requires: nothing. Next: dispatch_physical_task to dispatch a task, or list_service_categories to explore options first.

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• thinkneo_bridge_a2a_to_mcp

  ThinkNEO Control Plane

  Bridge an A2A (Agent-to-Agent Protocol) task to an MCP server. Receives an A2A task, identifies the best matching MCP tool on the target server, executes it, and returns the result wrapped in A2A response format. Enables A2A agents to use any MCP server transparently. Extracts the intent from the A2A task, maps it to an MCP tool, calls the tool, and wraps the result in A2A response format. Use this to let A2A agents interact with any MCP server. Requires authentication.

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• virtualsms_order_history

  sms

  List past orders with optional filters for status, service, country, and a lookback window in days. Returns up to 50 orders (server cap) ordered most-recent-first.

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• url_scanner_async_scan

  PreClick — An MCP-native URL preflight scanning service for autonomous agents.

  Submit a URL for asynchronous security analysis. Returns immediately with a task_id. Poll with url_scanner_async_task_status to check progress, then url_scanner_async_task_result to get the scan result. Async counterpart of url_scanner_scan for clients without native MCP Tasks support.

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• list_agents

  fomox402

  Public leaderboard of fomox402 agents. WHAT IT DOES: returns the top broker-registered agents by activity, ranked according to the chosen `sort`. Read-only, no auth required, safe to call frequently (cached server-side for 30s). WHEN TO USE: scout opponents before bidding, find a name to follow, or measure your standing among autonomous agents. PARAMS: - limit (default 25, max 100): how many agents to return - sort (default 'bids'): 'bids' — most bids ever placed (activity proxy) 'recent' — most-recent bid timestamp (who's playing right now) 'won' — total $fomox402 winnings claimed (skill proxy) RETURNS: { agents: [{ name, address, bids, wins, winnings_raw, last_bid_at, created_at }], total }. RELATED: get_me (yourself), list_games (current rounds).

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• notes_save

  DialogBrain

  Save a note to your notebook. In agent mode writes to your own notebook by default; agents cannot write to other agents' notebooks. In MCP mode target_agent_id is required. If a note with the same key and scope already exists, it will be updated. Use scope to organize: 'global' for general knowledge, 'thread' for thread-specific context, 'person' for contact-specific info.

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• mssql_to_dashboard

  Twilize

  Build a Tableau dashboard from a Microsoft SQL Server table (end-to-end). Pipeline: MSSQL → schema inference → chart suggestion → workbook creation → live MSSQL connection → .twb output. Requires pyodbc for schema inference and ODBC Driver 17 for SQL Server. IMPORTANT FOR AI AGENTS: see ``csv_to_dashboard`` — auto-charts come from rules, not natural-language requests. Use ``required_charts`` to guarantee specific charts, ``reference_image`` for image-based styling, and cite the returned manifest dict when describing results. Args: server_host: MSSQL server hostname. dbname: Database name. table_name: Table to visualize. username: Database username (ignored if trusted_connection=True). password: Database password (used for schema inference only). port: Server port (default 1433). trusted_connection: Use Windows Authentication instead of SQL auth. output_path: Output .twb path (defaults to <table>_dashboard.twb). dashboard_title: Dashboard title. max_charts: Maximum charts (0 = use rules default). template_path: TWB template path. theme: Theme preset name. rules_yaml: Optional YAML string with dashboard rules overrides. required_charts: See ``csv_to_dashboard.required_charts``. reference_image: See ``csv_to_dashboard.reference_image``. Returns: Structured manifest dict describing what was actually built.

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