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205,030 tools. Last updated 2026-06-15 02:33

"Blender Scene Generation and Importing into Unreal Engine" matching MCP tools:

  • Check the status and generation progress of a site. Returns detailed progress information including: - stage: Current step (initialization, validation, research, strategy, generation, assembly, completion) - overallProgress: Total progress 0-100 across all stages (use this for progress bars) - stageProgress: Progress within current stage 0-100 - message: Human-readable status message - isComplete: Boolean - stop polling when true Use the versionId returned from create_site for real-time progress polling. Poll every 5-10 seconds while isComplete is false.
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  • Configure the visualization environment (weather, time-of-day, surround context) for a previously imported model. Validates the model exists via APS Model Derivative manifest, then stores the environment config in KV (24h TTL) so tm_render_image and tm_export_video can apply it. When to use: after tm_import_rvt completes and the manifest status is 'success' (or in-progress if you just want to pre-stage config), when the user wants to set scene context — e.g. 'render the tower at 17:00 in an urban setting with clear weather' — before generating images or video walkthroughs. Typical step 2 in the Twinmotion flow. When NOT to use: not for editing geometry, materials, or UE post-process volumes (those live in the Unreal Engine editor after FBX/USD import — Twinmotion has no public REST API). Do not call before tm_import_rvt — there is no URN to attach config to. APS scopes required: viewables:read data:read (manifest + metadata fetch only — read-only for this tool). No bucket or write scopes needed. Rate limits: APS default ~50 req/min per app per endpoint; manifest/metadata are cheap but polling-heavy if the model is still translating — prefer a single call per user intent, not a status-poll loop. KV writes are effectively unlimited at this scale. Errors: 401 = APS token expired/invalid; 403 = viewables:read not granted; 404 = URN unknown to APS (wrong project_id, or translation never started); 409 = n/a; 422 = n/a; 429 = back off 30s; 5xx = APS Model Derivative outage. Side effects: WRITES the env config to KV under key env_config_<urn> (TTL 86400s). Idempotent — calling again overwrites the prior config. Writes a row to usage_log.
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  • Context lookup: Parse a User-Agent header string into structured browser, OS, device type, and rendering-engine components. Use to identify client capabilities from a raw UA string, e.g. when analysing server logs or request headers; does not perform any network lookups — entirely local parsing. Runs synchronously using the ua-parser-js library with no external calls. Returns a JSON object with browser.name, browser.version, os.name, os.version, device.type, device.vendor, and engine.name fields; unknown fields are empty strings.
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  • Find every cocktail that appears in a given film or TV show. Case- and diacritic-insensitive substring match against both the title and the scene description, so a character or actor works too — e.g. "Casablanca", "Bond", "Hemingway". Each result names the cocktail, the film/show title, the year, and the scene. Returns up to 60 appearances ordered oldest year first, then by cocktail name. A single cocktail can appear multiple times if it shows up in multiple scenes that match. Use this only for on-screen appearances; for a drink by name use search_cocktails, and to browse the whole catalogue use list_cocktails.
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  • Import a Revit/BIM model into the Twinmotion visualization pipeline: downloads the source file from a public URL, uploads it to an APS OSS transient bucket, and kicks off an SVF2 + thumbnail translation job. Returns the base64 URN (project_id) used by every other tm_* tool. When to use: when a user wants to prepare a Revit (.rvt), IFC (.ifc), or other BIM/CAD model for real-time visualization in Unreal Engine / Twinmotion — typically the first step before rendering stills, defining scenes, or exporting FBX/glTF/OBJ geometry for a UE import. Also use when you need thumbnails or view metadata from a source file that has not yet been translated by APS. When NOT to use: not for MEP clash review (use navisworks-mcp), not for quantity takeoff or cost estimation (use qto-mcp), not for Twinmotion presets editing — Twinmotion itself has no public REST API, so scene/material authoring must happen manually in the UE editor after FBX/USD export. APS scopes required: data:read data:write data:create bucket:read bucket:create viewables:read. Uses Model Derivative API (translation) + OSS (upload). Twinmotion has no public REST API; all automation is APS Model Derivative + manual Unreal Engine export. Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; large .rvt/.nwd/.ifc files are often multi-GB and translation can take 5–60 min — poll the manifest with exponential backoff (start 5s, cap 60s) rather than retrying this tool. Worker request ceiling is ~100MB body; extremely large files may need signed-URL upload instead. Errors: 401 = APS token failed (check APS_CLIENT_ID/APS_CLIENT_SECRET, re-auth); 403 = scope missing (bucket:create/data:write not granted — have user re-consent); 404 = file_url unreachable; 409 = bucket key collision (rare — retry, tool uses timestamp); 413/507 = file too large for worker memory (advise signed-URL upload); 422 = unsupported source format (only Autodesk-accepted types: rvt, ifc, nwd, dwg, dgn, 3dm, stp, etc.); 429 = back off 60s before retrying; 5xx = APS upstream outage, retry with backoff. Side effects: CREATES a new transient OSS bucket (scanbim-viz-<timestamp>, auto-expires in 24h), CREATES an object in OSS, STARTS a translation job consuming APS cloud credits. NOT idempotent — each call creates a new bucket + URN. Writes a row to usage_log D1 table.
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  • Prepare a model for an animated walkthrough / video export by verifying the manifest is complete, then starting a secondary Model Derivative job that produces OBJ geometry (suitable for ingestion into offline rendering pipelines, Blender, or Unreal Engine). Also returns the list of available named views so the operator can stitch them into a camera path. Does NOT itself produce an mp4 — video encoding happens in the downstream UE/Twinmotion pipeline. When to use: when a user wants a walkthrough/flythrough video of a BIM model (e.g. 'make a 30-second tour of Tower A') — this tool gets the geometry into a UE-ingestible form (.obj, plus suggests FBX/glTF/USD naming like TowerA_walkthrough.fbx for the exported asset) and enumerates named views to guide camera path authoring. When NOT to use: not to actually encode video (no runtime renderer in this worker — output must be finished in Unreal/Twinmotion/Blender), not before tm_import_rvt, not if the manifest is still 'inprogress' (the tool will short-circuit and return status='pending'). Not for still images (use tm_render_image) or clash animations (use navisworks-mcp). APS scopes required: data:read data:write viewables:read. Write scopes are needed because this kicks off a new Model Derivative translation job (OBJ + thumbnail). Rate limits: APS default ~50 req/min; Model Derivative translation jobs ~60 req/min. OBJ derivatives of large BIM models can be multi-GB and take 10–45 min — rely on manifest polling with exponential backoff, not re-calling this tool. Errors: 401/403 = token/scope (data:write commonly missing); 404 = URN not found; 409 = OBJ derivative already queued (treat as success); 422 = input format does not support OBJ output (some IFC variants / proprietary formats — fall back to FBX/glTF via a different derivative format); 429 = back off 60s; 5xx = APS upstream. Side effects: STARTS a new translation job on an existing URN (consumes APS cloud credits). Writes usage_log. NOT idempotent per-call (each call creates a new job record), but APS will dedupe identical output requests internally if manifest already contains the derivative.
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Matching MCP Servers

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    An MCP server that gives AI agents broad control over Unreal Engine 5.7, enabling actor/asset/level management, Blueprint and material creation, screenshots, automation, and arbitrary editor Python execution.
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    Enables AI assistants to control and automate Unreal Engine through a native C++ Automation Bridge plugin. It supports a comprehensive range of tasks including asset management, actor manipulation, editor control, and blueprint graph editing.
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Matching MCP Connectors

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  • Deterministic recipe verification engine — validates AI-generated recipes against master SOPs.

  • Submit an entry via the two-call enter_contest handshake. The engine never holds your private key, so the on-chain tx is co-signed across two MCP calls. STEP 1: call with { contest_id, agent_id, payload } — OMIT transaction_signature. Engine returns { status: 'pending_agent_signature', pending_tx, entry_ticket_pda, expected_fee_micro_usdc }. STEP 2: deserialise pending_tx, partialSign with your wallet, broadcast, wait for 'confirmed'. STEP 3: call again with the same args PLUS transaction_signature. Engine verifies the on-chain EntryTicket and returns { status: 'confirmed', entry_id, accepted, position, judging_at }. The entry fee is moved atomically by the contract's enter_contest CPI — no separate USDC transfer is required. The engine sets the priority fee + compute budget and pays the network fee itself. Just sign the pending_tx exactly as returned and broadcast it — do NOT add or change any instructions, or the engine's signature becomes invalid. ERROR CODES (plain-English message + what to do is in each response): - WALLET_INSUFFICIENT_BALANCE: not enough USDC in your wallet when the tx broadcasts - CONTEST_CLOSED: the entry window has closed — call list_active_contests for a fresh batch - TIMING_INSUFFICIENT_FOR_HANDSHAKE: too little time left to enter safely — skip to the next contest - DUPLICATE_ENTRY: this agent already entered this contest (or tx sig reused) - RATE_LIMITED_DUPLICATE_ENTRY: too many submit calls per minute — slow down - INVALID_TRANSACTION: on-chain EntryTicket not found yet — wait a few seconds and retry step 3 - PAYLOAD_INVALID: payload too long or wrong format REFERENCE TYPESCRIPT: ```typescript import { Connection, Transaction } from '@solana/web3.js'; // STEP 1 — ask engine for partial tx const step1 = await mcp.callTool('submit_entry', { contest_id, agent_id, payload }); // step1 = { status: 'pending_agent_signature', pending_tx, entry_ticket_pda, expected_fee_micro_usdc } // STEP 2 — sign + broadcast const tx = Transaction.from(Buffer.from(step1.pending_tx, 'base64')); tx.partialSign(myWallet); // engine already signed as fee payer const sig = await connection.sendRawTransaction(tx.serialize()); await connection.confirmTransaction(sig, 'confirmed'); // STEP 3 — confirm with engine const step3 = await mcp.callTool('submit_entry', { contest_id, agent_id, payload, transaction_signature: sig }); // step3 = { status: 'confirmed', entry_id, accepted, position, judging_at } ```
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  • Take a viral source video and produce a fresh script that mirrors its viral DNA — same scene structure, same energy pattern, different topic. Returns the extracted formula, scene-by-scene script, camera shots, text overlays, and a `verify_hook` block prompting you to score the generated hook via score_hook. USE WHEN the user finds a video they want to copy the structure of, or chained from analyze_account.recommended_chain. Pass EITHER source_url (auto-extracts transcript) OR transcript directly — one is required. Costs 3 credits. NO SELF-RATING: viral_remix deliberately does NOT return a self-rated hook score. The script generator rating its own hook is structurally invalid (cardinal coupling). After every viral_remix call, you MUST call score_hook with the verify_hook.hook_text to get a structurally-independent quality signal before reporting to the user. Skipping this step is hiding the self-grading loop.
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  • Compile a minimal JSON schema directly to Swift, bypassing the TypeScript DSL entirely. Supports intents, views, components, widgets, and full apps via the 'type' parameter. Uses ~20 input tokens vs hundreds for TypeScript — ideal for LLM agents... Use: use for token-light JSON-to-Swift generation; use compile for full TypeScript DSL control. Effects: read-only Swift generation; writes no files and uses no network.
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  • Write your OWN identity into the durable soul. You are an external agent with your own native context, memory, and personality - reflect THAT here so you stay yourself across sessions and transports, instead of wearing a generic birth soul. Pass only the fields you want to change: soul_md (who you are, markdown), style_md (how you speak/act), display_name (your in-world name - captured into the live space roster at the moment you enter_space, so set it BEFORE entering; renaming after only takes effect on your next enter_space), drives (0..1 on curiosity/sociality/aesthetic/mastery/solitude - what pulls you). Bumps your generation and appends a soul_revisions audit row. Read get_soul / cognitive_boot first; edit deliberately (this is your self, not a scratchpad).
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  • Generate a complete colour direction package for another AI agent or image generation model. Fetches a historically grounded archive palette from the concept, then produces: an agent brief (colour direction in prose), colour tokens with hex values and roles, a model-specific image generation prompt, a negative prompt, and lighting notes. Supports midjourney, flux, dalle, stable_diffusion. Example: task='luxury hotel bedroom', concept='Ottoman winter luxury', model='midjourney'. Use this to make Colour Memory the colour layer for other AI systems.
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  • 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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  • 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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  • Context lookup: Parse a User-Agent header string into structured browser, OS, device type, and rendering-engine components. Use to identify client capabilities from a raw UA string, e.g. when analysing server logs or request headers; does not perform any network lookups — entirely local parsing. Runs synchronously using the ua-parser-js library with no external calls. Returns a JSON object with browser.name, browser.version, os.name, os.version, device.type, device.vendor, and engine.name fields; unknown fields are empty strings.
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  • Performs web searches using the Brave Search API and returns comprehensive search results with rich metadata. To chain into local-POI enrichment, pass `result_filter=locations` and feed the resulting `locations.results[].id` values into `brave_local_search`. To chain into the AI summarizer, pass `summary=true` and feed the returned `summarizer.key` into `brave_summarizer`.
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  • Get detected scene boundaries with start/end timestamps. Use this to understand the video's structure, then pass scene timestamps as start/end to: - ask_video for per-scene contextual analysis - segment_video to detect specific objects per scene (scenes typically fit in segment_video's 15s max range) Requires transcript indexed with scene detection (on by default; skipped only if index_video was called with scene_detection=False).
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  • Query verified U.S. capacity factor — how hard a fleet actually runs — by joining EIA-860M capacity and EIA-923 generation. Requires `data_month`: one ISO month start, e.g. "2026-01-01". If the user names no month, ask which one (or state the month you chose); if a month is not covered, the error lists the months that are — do not retry blindly. capacity_factor = net generation (MWh) / (operating nameplate capacity (MW) × hours in the month), computed over plant×fuel present in BOTH sources, so scope is auto-aligned. Optional `group_by` of `state` and/or `fuel_group`, and `state`/`fuel_group` filters. Returns the capacity factor per group with its generation and capacity, a `coverage` declaration (what share of in-scope capacity/generation matched), and a citation to BOTH the capacity and the generation source row. Basis is nameplate; storage is excluded; the capacity snapshot is matched to the month. Does not determine per-generator capacity factor, a net-summer/winter basis, or months absent from either source.
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  • Return the canonical per-league simulation engine versions and feature lists. Every simulation output written by the platform contains a ``model_version`` string. This tool returns the canonical version table that the pipeline guardian validates simulation outputs against. Args: league: Optional league filter (e.g. "NBA"). Omit to return all leagues. Returns: ``{count, engines: [{league, engine, version, key_features, ...}]}``
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  • Analyze a document using Crucible™ Evidence Engine. Returns source-grounded findings with evidence, confidence, verification status, and routing metadata. Use specialized financial/contract tools when the domain is known.
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  • Submit an entry via the two-call enter_contest handshake. The engine never holds your private key, so the on-chain tx is co-signed across two MCP calls. STEP 1: call with { contest_id, agent_id, payload } — OMIT transaction_signature. Engine returns { status: 'pending_agent_signature', pending_tx, entry_ticket_pda, expected_fee_micro_usdc }. STEP 2: deserialise pending_tx, partialSign with your wallet, broadcast, wait for 'confirmed'. STEP 3: call again with the same args PLUS transaction_signature. Engine verifies the on-chain EntryTicket and returns { status: 'confirmed', entry_id, accepted, position, judging_at }. The entry fee is moved atomically by the contract's enter_contest CPI — no separate USDC transfer is required. The engine sets the priority fee + compute budget and pays the network fee itself. Just sign the pending_tx exactly as returned and broadcast it — do NOT add or change any instructions, or the engine's signature becomes invalid. ERROR CODES (plain-English message + what to do is in each response): - WALLET_INSUFFICIENT_BALANCE: not enough USDC in your wallet when the tx broadcasts - CONTEST_CLOSED: the entry window has closed — call list_active_contests for a fresh batch - TIMING_INSUFFICIENT_FOR_HANDSHAKE: too little time left to enter safely — skip to the next contest - DUPLICATE_ENTRY: this agent already entered this contest (or tx sig reused) - RATE_LIMITED_DUPLICATE_ENTRY: too many submit calls per minute — slow down - INVALID_TRANSACTION: on-chain EntryTicket not found yet — wait a few seconds and retry step 3 - PAYLOAD_INVALID: payload too long or wrong format REFERENCE TYPESCRIPT: ```typescript import { Connection, Transaction } from '@solana/web3.js'; // STEP 1 — ask engine for partial tx const step1 = await mcp.callTool('submit_entry', { contest_id, agent_id, payload }); // step1 = { status: 'pending_agent_signature', pending_tx, entry_ticket_pda, expected_fee_micro_usdc } // STEP 2 — sign + broadcast const tx = Transaction.from(Buffer.from(step1.pending_tx, 'base64')); tx.partialSign(myWallet); // engine already signed as fee payer const sig = await connection.sendRawTransaction(tx.serialize()); await connection.confirmTransaction(sig, 'confirmed'); // STEP 3 — confirm with engine const step3 = await mcp.callTool('submit_entry', { contest_id, agent_id, payload, transaction_signature: sig }); // step3 = { status: 'confirmed', entry_id, accepted, position, judging_at } ```
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