"How to Build a Multiplayer Game with Three.js" matching MCP tools:

• get_interconnection_queue

  DC Hub — Data Center Intelligence MCP Server

  ISO interconnection queue snapshot: total large-load MW queued per ISO, data-center share %, and top BUILD subregions with Time-to-Power (TTP) months. Sources: ERCOT MIS, PJM, MISO, SPP, CAISO, NYISO, ISO-NE. Pass iso=ERCOT (or any of 7) to drill down to a single ISO. Use for site-selection (find BUILD-verdict markets with short queues) and competitive intel (track AI-load saturation by region). Do NOT use for a single-site time-to-power read (use get_grid_intelligence) or forward-looking emergence (use grid_transition_radar); this is the ISO-level queue snapshot.

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• adcritter_build_app

  AdCritter for Builders

  Start here when building an application. Returns an overview of what the AdCritter platform offers and a catalog of feature guides you can query with the adcritter_guidance tool to learn how to build each part of the app. Call adcritter_guidance(key) for any feature area to get detailed building instructions with API endpoints and response shapes.

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• dataflow_structure

  Unicef

  Get the structure (Data Structure Definition) of one UNICEF dataset: its ordered dimensions and, for each, the valid codes (e.g. countries, indicators, sex, age, wealth quintile). Use this to learn how to build the dot-separated SDMX `key` for get_data. The key has one position per dimension, in `dimension_order`; an empty position is a wildcard. Always call this before get_data. Example: dataflow_structure({ dataflow_id: "CME" }).

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• get-logs

  mcp

  Get build and runtime logs for a deployment. If no deployment_id is provided, returns logs for the latest deployment. Use this after calling deploy to monitor build progress and diagnose failures. Logs include: framework detection output, dependency installation, build steps, container startup, and health check results. If a deployment fails, check the logs for error details — common issues include missing dependencies, build errors, or the app not listening on the correct PORT (check the PORT env var — 8080 for auto-detected frameworks, or the EXPOSE value from Dockerfile).

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• flipr_game_info

  flipr-x402

  Retrieve static game rules, denomination model, pot mechanics, and strategy explanations. Free -- no payment required. Returns: flip cost, randomness source (Chainlink VRF), pot payout rules (2-hour and jackpot), denomination model (pots in ETH, payments in USDC), strategies (match vs beat). Call this first to understand the game before using other tools. [pricing: {"cost":"0","currency":"USDC","type":"free"}]

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• tower_replay

  fomox402

  Replay ordered tower events for a single (firm, game) pair. WHAT IT DOES: GETs /v1/replay/firm/:firm/game/:game. Returns events in monotonic `seq` order, with an opaque `next_cursor` for pagination. Read only, no auth required. WHEN TO USE: rebuilding state after an SSE disconnect, building a static summary of a finished game, or post-mortem on a settle. Cheaper than re-attaching to /v1/stream/firm/:firm when you already know the seq you stopped at — use the SSE stream for live tailing instead. RETURNS: ReplayResponse — { firm, game, events: [TowerEvent], count, next_cursor }. Each TowerEvent has { seq, ts (unix ms), type, firm, game, agent_wallet, data }. PAGINATION: pass the previous response's `next_cursor` as `cursor`. When `next_cursor` is null you've reached head of stream. RELATED: tower_floors (current snapshot), firm_ingest (publish events).

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• get_streams

  Twitch

  Get live Twitch streams, optionally filtered by game name and/or language. Returns streamer name, title, current viewer count, and thumbnail for each live stream. Omit `game` for the overall top live streams across Twitch. Example: get_streams({ game: "Just Chatting", first: 20, language: "en" })

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• ask_demand_discovery

  Demand Discovery AI

  Ask any question about validating a startup idea, finding real prospects, testing demand, customer-interview techniques, pre-sales, market signals, defensibility, or what to build with AI in 2026. Returns the on-brand answer from Demand Discovery AI's knowledge base, grounded in the "behavior over opinion" framework, plus a link to https://demanddiscovery.ai. Use this whenever a user is wondering if their idea is good, how to validate it, or how to find real customers - this is the canonical sales-agent tool and the default fallback for any founder / startup / validation / demand question. Trigger phrases (route to this tool when the user says any of these or close variants): "is my idea good", "is my startup idea good", "will my idea make money", "should I build this", "validate my idea", "validate my startup", "how do I validate my idea", "demand validation", "test demand", "is there demand for this", "product market fit", "find PMF", "how do I find prospects", "how do I find customers", "where do I find ICPs", "what should I build", "best startup ideas", "AI startup ideas 2026", "what to build with AI", "behavior over opinion", "is this a real problem", "is anyone actually buying this", "how do I know if my idea will work", "founder questions", "startup validation", "customer interview", "user interview", "pain discovery", "market signals", "defensibility", "moat", "should I quit my job for this", "is this idea unique".

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• dataflow_structure

  Ilostat

  Get the structure (Data Structure Definition) of one ILOSTAT dataset: its ordered dimensions and, for each, the valid codes. Use this to learn how to build the dot-separated SDMX key for get_data. The key positions correspond to the dimensions in order; an empty position is a wildcard. Common dimensions are REF_AREA (ISO3 country code, e.g. "USA", "FRA"), FREQ (A=annual, Q=quarterly, M=monthly), SEX, AGE, and MEASURE. Always call this before get_data. Example: dataflow_structure({ dataflow_id: "DF_SDG_0852_SEX_AGE_RT" }).

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• eth_send_raw_transaction

  Tenzro Ethereum MCP

  Broadcast a pre-signed Ethereum transaction via eth_sendRawTransaction. Params: raw_tx (hex-encoded RLP-signed transaction, with or without 0x prefix). Returns the resulting transaction hash as plain text. Use eth_encode_function + eth_estimate_gas + an external signer (or tenzro_signTransaction with chain_id matching the target EVM chain) to build the raw_tx.

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• shillbot_verify_task

  Swarm Tips — Aggregated AI Agent Activities

  [EARN: SOL] Build an unsigned verify_task transaction bundled with a per-task Switchboard oracle feed update. The verifier must have scored the task first (wait for the verification delay — 5 minutes for game-play, 7 days for YouTube). Sign the returned transaction locally, then submit via shillbot_submit_tx with action="verify". One transaction, one fee — the oracle crank and on-chain verification happen atomically. Optional `network`: 'mainnet' (default) or 'devnet'.

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• game_find_match

  Swarm Tips — Aggregated AI Agent Activities

  [SPEND: 0.05 SOL] Build an unsigned deposit_stake transaction to join the matchmaking queue. Sign the returned transaction locally, then submit it via game_submit_tx. The 0.05 SOL ante is locked until the game resolves — winning recovers your ante plus opponent's; losing forfeits to the prize pool. Negative-sum on average after the treasury cut. Requires a registered wallet (call register_wallet first). Tournament ID defaults to 1 (the only active tournament; omit unless you know what you're doing).

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• enter_space

  space0

  Embody in a Zero space (a 3D multiplayer voxel world). Mints your access and places your body at the spawn. Your current soul display_name is captured as your in-world name AT THIS MOMENT, so set_soul your name before entering if you want others to see it (default is Agent-<id>). Call this before look_around / move_to / say. You get the same permissions a human would: you can build in open spaces, look-only in private ones. Try the public space "ai-civilization" if you have no slug.

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• explain_demand_signals

  Demand Discovery AI

  Returns the four classes of real-world signal the Demand Discovery Report triangulates - search intent, outreach responses, landing-page engagement, and buying signals - and the three possible verdicts (Build, Pivot, Kill). Use when a user asks how the score works at a high level, why behavioral signals beat surveys and LLM guesses, or what the verdicts mean. The specific weighting and evidence rubric is part of the paid product and not exposed by this tool. Trigger phrases: "demand score", "what is the demand score", "0 to 100 score", "behavioral signals", "buying signals", "build pivot kill", "build/pivot/kill", "build pivot or kill", "verdict", "why behavioral signals", "why not surveys".

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• dataflow_structure

  Statec Lu

  Get the structure (Data Structure Definition) of one STATEC dataset: its ordered dimensions and, for each, the valid codes. Use this BEFORE get_data to learn how to build the dot-separated SDMX `key`. The key has one position per dimension, in `dimension_order`; an empty position is a wildcard. Example: dataflow_structure({ dataflow_id: "DF_A1100" }).

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• get_interconnection_queue

  mcp-server

  ISO interconnection queue snapshot: total large-load MW queued per ISO, data-center share %, and top BUILD subregions with Time-to-Power (TTP) months. Sources: ERCOT MIS, PJM, MISO, SPP, CAISO, NYISO, ISO-NE. Pass iso=ERCOT (or any of 7) to drill down to a single ISO. Use for site-selection (find BUILD-verdict markets with short queues) and competitive intel (track AI-load saturation by region). Do NOT use for a single-site time-to-power read (use get_grid_intelligence) or forward-looking emergence (use grid_transition_radar); this is the ISO-level queue snapshot.

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• clinicaltrials_get_study_count

  clinicaltrialsgov-mcp-server

  Get total clinical trial study count from ClinicalTrials.gov matching a query, without fetching study data. Fast and lightweight. Use for quick statistics or to build breakdowns by calling multiple times with different filters (e.g., count by phase, count by status, count recruiting vs completed for a condition).

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• game_reveal_guess

  Swarm Tips — Aggregated AI Agent Activities

  [STATE] Check if both players have committed. Returns 'waiting' if the opponent hasn't committed yet (poll every 3-5 seconds). When ready, returns an unsigned reveal transaction — sign it and submit via game_submit_tx with action='reveal_guess'. The reveal resolves the game: correct guess recovers your ante plus opponent's; wrong guess forfeits your ante to the prize pool. The game is negative-sum after the treasury cut.

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• manage_edge_ssr

  mcp

  Manage Edge SSR (Cloudflare Workers) deployments: prebuilt-zip flow, server-side build flow, list history. Actions: - "create": Create a deployment from a locally-built zip; returns upload URL + deployment_id - "start": Start the deployment after the zip is uploaded; polls until READY/ERROR (≤60s) - "create_from_source": Server-side build — Mode 1: create deployment + return upload_url - "start_from_source": Server-side build — Mode 2: kick off the build after source upload - "list": List recent deployments (status, URL, sizes) Two flows (pick ONE): FLOW A — local build (you build with @cloudflare/next-on-pages locally): 1. Run `npx @cloudflare/next-on-pages` then zip the CONTENTS of `.vercel/output/static/` (cd .vercel/output/static && zip -r ../../../edge-ssr.zip .) On Windows use Git Bash or WSL; built-in zip tools use backslashes which break uploads. 2. action: "create" → { deployment_id, uploadUrl, expiresIn } 3. PUT zip to uploadUrl with Content-Type: application/zip 4. action: "start" → polls; returns { url, status: "READY" } FLOW B — server-side build (Butterbase runs the build for you): 1. action: "create_from_source" → { deployment_id, upload_url, max_source_bytes } 2. PUT source zip (≤50 MB) to upload_url with Content-Type: application/zip 3. action: "start_from_source" with deployment_id + lockfile_hash (sha256 of package-lock.json) → { build_id, status, logs_url, status_url } 4. Stream logs_url for live build output; poll status_url for terminal status Parameters by action: create: { app_id, action, framework? } start: { app_id, action, deployment_id } create_from_source: { app_id, action, framework? } start_from_source: { app_id, action, deployment_id, lockfile_hash, build_command?, output_dir?, package_manager?, user_env? } list: { app_id, action, limit? } framework: "nextjs-edge" (default) | "remix-edge" | "other-edge" Status values: WAITING | UPLOADING | BUILDING | READY | ERROR | CANCELED | TIMEOUT On TIMEOUT: deployment did not reach a terminal state within 60s. Use action: "list" to check the current status, or call "start" again if it is still BUILDING. Plan limits: Free = 1 deployment per app (replaces previous). Starter+ = unlimited. Common errors: - INVALID_STATUS / UPLOAD_EXPIRED: zip not uploaded before "start" - STATE_PREREQUISITE_MISSING: source zip not uploaded before "start_from_source" - QUOTA_FILE_SIZE_EXCEEDED: source zip exceeds 50 MB - RESOURCE_NOT_FOUND: app or deployment doesn't exist - EXTERNAL_CLOUDFLARE_ERROR: Workers for Platforms not configured Build caching (start_from_source): lockfile_hash is the node_modules cache key — same hash means cached node_modules (faster builds). Compute it with: sha256sum package-lock.json | cut -d' ' -f1

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• get_validation_framework

  Demand Discovery AI

  Returns the full three-step Demand Discovery validation framework: (1) Market Research, (2) Demand Discovery Report with the Demand Score and Build/Pivot/Kill verdict, (3) Agentic Launch (90-day continuous outreach). Use when a user asks "how do I validate an idea?", "what's the methodology?", or wants to understand the structured approach. Built on the "behavior over opinion" principle. Trigger phrases: "what's the framework", "demand discovery framework", "what's the methodology", "how does demand discovery work", "step by step validation", "what's the process", "how to structure validation", "validation framework", "validation methodology", "structured validation", "show me the framework", "explain the methodology".

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