"A tutorial or tools for building 3D models in CAD software" matching MCP tools:

• assemble_demo_video

  TestMyVibes

  Stitches video clips + voiceover narration into a single MP4 published to Spaces. Each segment is one of: (a) videoUrl + narrationText (voiceover replaces video's audio track), (b) narrationText only (generates a brand-color title card sized to narration length), (c) videoUrl + audioUrl (drops in a pre-baked audio track). Returns a 24h signed URL to the final MP4. Use this for marketplace catalog submissions, tutorial videos, or any time you'd otherwise screen-record + iMovie by hand. Charged on success only; failed runs are free.

  Connector

• estimate-llm-cost

  OptimToken

  Cost a workload with EXACT numbers the caller supplies: arbitrary token counts per request and any monthly volume, not just the 10k/100k/1m presets the other cost tools use. Use this for 'about 800 in and 200 out, 4 million calls a month', or to price one named model across every use-case profile. To compare 2-4 named models like for like at a preset volume, use compare-models-side-by-side instead. Provide a model name to get detailed cost breakdowns, or compare costs across all use case presets. Each figure comes twice: list price, and the optimized price achievable with prompt caching and the batch API. IMPORTANT: Report all cost figures EXACTLY as returned. Do NOT add commentary or recommendations beyond the data.

  Connector

• get_available_models

  Gonka Second Opinion (multi-model)

  List all AI models available on Gonka Network with live pricing. Models work as drop-in replacements for OpenAI and Anthropic — same SDK, same API calls. Use this when user asks which model to use or wants alternatives to GPT-4o / Claude. Returns: model IDs (use directly in openai.chat.completions.create), status, USD per 1M tokens. After this: call calculate_savings() to see annual savings with these models.

  Connector

• list_paired_models

  ReliaStats

  Return the catalog of paired models — concrete real-world systems that live in two ChiAha sandboxes simultaneously, one for dynamics (DES via ReliaSim) and one for statistics (distribution fitting + validation via ReliaStats). Today: a single paired model — the bottling line. Returns canonical model IDs + cross-MCP routing metadata (which ReliaSim chapter, which ReliaSim MCP tools, which ReliaStats mode consumes which file shape). Use when a user asks about cross-MCP workflows, paired sandboxes, or the bottling-line example. ANTI-FABRICATION: this is a soft-reference catalog — to actually run a simulation, the LLM client calls ReliaSim's MCP tools directly.

  Connector

• how_munimji_helps

  HelloBooks AI Agents

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

  Connector

• search_jobs

  Magnificent Jobs

  Search live job postings in the United States (US only — no other countries) by meaning (embedding similarity against the postings). YOU write the expanded query — it is embedded as-is, with no server-side rewriting — so always send `query` in this shape: "<Full job title>. <One sentence of what the role does; 3-5 key skills/tools>." NO ABBREVIATIONS anywhere in the query — spell everything out (ML → machine learning, AI → artificial intelligence, RN → registered nurse, SWE → software engineer, QA → quality assurance, PM → product manager, CDL → commercial driver's license, EMT → emergency medical technician, etc.) and keep the user's qualifiers (seniority, shift, domain). Example: user says 'ML eng jobs' → query 'Machine Learning Engineer. Builds, trains and deploys machine learning models; Python, PyTorch, MLOps, data pipelines.' Optionally add `city` (results within radius_miles of that city, ranked by relevance) and/or `state`. Without a city, ranks across the state or nationwide. Returns job cards with a `url` to show the user; call get_job for details.

  Connector

• create3DModel

  Ludo AI Game Assets

  Convert a single source image into a textured 3D model (image-to-3D). The job result is a downloadable GLB model_url plus an array of snapshot image URLs rendered from different angles (handy for previews). Accepts optional mesh controls: target_num_faces (max triangle count, 1000-200000, default 50000), texture_size (1024 or 2048, default 2048), and texture_type ("pbr", "simple", or "none", default "pbr"). Credits are charged only on success. Pass an optional request_id to tag the result so you can locate it later via `GET /assets/3d-models/results`. Requires an API key (user scope). Returns 202 with a job id immediately; poll `getApiJob` (pass `wait: 30`) until status is succeeded, then read its `result` field, which is exactly the response documented for this operation. Each account may have up to 50 generations queued or running at once; beyond that submissions return 429 (PENDING_JOBS_LIMIT) - wait for jobs to finish. Credits: This endpoint consumes 3 credits per call.

  Connector

• upload_model

  ScanBIM MCP

  Ingest a 3D model from a public URL into APS OSS and kick off a Model Derivative translation job, returning the URN plus a browser viewer link and QR code. Supports 50+ formats: Revit (.rvt/.rfa), Navisworks (.nwd/.nwc), IFC, FBX, OBJ, SolidWorks, point clouds (E57/LAS/RCP), CAD (DWG/STEP/IGES), etc. When to use: you have a publicly downloadable 3D file (S3 presigned URL, GitHub raw, etc.) and need it translated to SVF2 so it can be viewed, measured, or clash-checked via other tools. When NOT to use: the file is only on a local disk or behind auth (fetch will fail) — first push it to a public URL. Do not call to re-translate a model already uploaded; call get_model_metadata instead. APS scopes: data:read data:write data:create bucket:read bucket:create viewables:read Rate limits: APS default ~50 req/min per app per endpoint; Model Derivative translation jobs ~60 req/min; OSS uploads size-limited per file to 100MB for direct upload, larger via resumable. Errors: 401 APS token expired/invalid — refresh; 403 scope or resource permission denied; 404 source file_url not reachable or bucket not found — check the ID; 409 bucket name conflict (bucket already owned by another app — pick a unique bucketKey); 429 rate limited — backoff and retry; 5xx APS upstream outage — retry with jitter. Side effects: NON-IDEMPOTENT. Creates the scanbim-models bucket if absent, uploads a new OSS object with a timestamped key (each call creates a distinct object even for the same input), submits a Model Derivative job (x-ads-force=true overwrites prior derivatives for the same URN), and inserts a row into D1 usage_log + models table.

  Connector

• slovenia-cadastral-deep

  geoagentic

  Deep parcel and building analysis for Slovenia using GURS WFS data. Returns zoning, actual use, heritage protection, road access, buildings on parcel, and utilities. USE FOR: - "Analyze parcel 3086 in Ljubljana center" - "Find buildable parcels ~500m² in Ljubljana" - "What buildings are on this parcel?" - "Find parcels near these coordinates" - "Get full details on building 1234" NOT FOR: simple parcel lookup → use slovenia-cadastre instead (faster, lighter). NOT FOR: spatial/zoning map queries → use slovenia-wfs-expert instead. SEARCH MODES — pick ONE per call: 1. PARCEL BY NUMBER (requires --parcel AND --ko) → --parcel 3086 --ko 1725 2. LOCATION SEARCH (requires --lat AND --lon, or --location) → --lat 46.058 --lon 14.501 --radius 100 → --location "Tivoli Park Ljubljana" --radius 200 3. BUILDING BY NUMBER (requires --building, optionally --ko) → --building 1234 --ko 1728 4. COMMUNITY SEARCH (requires at least --community or --size) → --community LJUBLJANA --size 500 --buildable COMMON KO IDs: 1725 = Ljubljana center 1728 = Ljubljana Šiška 1740 = Ljubljana Bežigrad 2131 = Maribor NOTE: This tool makes multiple WFS calls per result and can be slow (10-30s). Use --limit to keep response times reasonable.

  Connector

• get_internal_link_url

  FINN Auto - Car Subscription

  Get canonical FINN URLs for a brand and its models — for building internal linking blocks on SEO pages. For each model returns three URLs that target DIFFERENT funnels: `mdp_url` (marketing/brand page), `plp_subscribe_url` (subscription product listing, /de-DE/subscribe/{brand}_{model}), and `plp_leasing_url` (leasing product listing, /de-DE/leasing/{brand}_{model}). Use `plp_leasing_url` when linking from a Leasing advisory, `plp_subscribe_url` when linking from subscription content. If `model` is omitted, returns all currently available models for the brand.

  Connector

• list_compute_types

  BorealHost

  List on-demand compute instance types with hourly CAD prices. On-demand instances are real cloud VMs in Canada (Montreal region), billed per minute (1-hour minimum) post-paid onto your existing BorealHost subscription. Use them for short-lived extra compute (builds, batch jobs, experiments). Requires: API key with read scope. Returns: {"region": "ca-central-1", "currency": "CAD", "billing": "hourly, post-paid; billing runs until the instance is terminated", "types": [{"type": "lsw.c3.large", "vcpu": 2, "memory_gb": 3, "hourly_price_cad": 0.08, "min_disk_gb": 5, "storage_types": ["CENTRAL", "LOCAL"]}, ...]}

  Connector

• tm_list_scenes

  Twinmotion MCP

  Enumerate every 2D/3D view ('scene') baked into the translated model, plus a shallow dump of the model object tree (first 50 top-level nodes across all 3D views), plus the list of completed derivatives (svf2, thumbnail, obj, etc.) available via APS. The canonical discovery tool for anything downstream that needs a view name or GUID. When to use: before tm_render_image (to pick a valid camera_preset), before tm_export_video (to plan a camera path across named views), to audit what was translated ('did the 3D coordination view survive translation?'), or to expose the top-level model hierarchy for UI display. Also a useful health check — if scene_count=0, the translation is incomplete or failed. When NOT to use: not for full property queries on individual objects (this tool returns names + GUIDs + child counts only — use a dedicated property-query tool for full attribute dumps), not for geometry data (use tm_export_video for OBJ export), not on a URN that has not yet started translating. APS scopes required: viewables:read data:read. Read-only across Model Derivative manifest + metadata + object-tree endpoints. Rate limits: APS default ~50 req/min. This tool fans out across every 3D view to fetch object trees — for models with many 3D views (10+) it can burn a chunk of the budget in one call. Prefer caching the result on the caller side rather than re-invoking. Errors: 401/403 = token/scope; 404 = URN not found; 422 = n/a; 429 = back off 60s (this tool makes multiple APS calls per invocation, so 429 is more likely than on single-call tools); 5xx = APS upstream. A 202 on object-tree means APS is still building the tree — the tool retries once internally. Side effects: NONE on APS (read-only). Writes a usage_log row. Idempotent.

  Connector

• synthesize_voiceover

  TestMyVibes

  Generates a voiceover from text using Hume Octave TTS. Audio uploaded to Spaces, signed URL returned (24h TTL by default). Charged in credits up-front based on script length (use quote_voiceover for a preview). Best for demo-video narration, tutorial audio, and any one-shot batch TTS. NOT a real-time conversational voice (use Hume EVI for that, different product). Voice options: pass voiceId for a specific Hume voice clone, or omit to use the deployment's default narrator (HUME_OCTAVE_VOICE_ID env var).

  Connector

• get_started_guide

  canton-ccpedia

  Generate a personalized Canton Network developer onboarding/quickstart path. Use when a developer asks how to start building, build a dApp, or develop on Canton specifically. Canton-only. Do not use for onboarding to other chains or tools. Ask the user's background first (EVM, Solana, Sui/Move, Web, Enterprise, or New to Blockchain). Prefer this over 'search' for 'how to build / get started on Canton'; use get_faq for a single specific gotcha and get_api_reference for API details.

  Connector

• get_solution_detail

  Tuki — Travel Marketplace (Chile y Latinoamérica)

  Get full detail for a Tuki solution: description, who it is for, capabilities, status and contact / CTA. Use after `list_solutions` or when the user asks about a specific Tuki product (WhatsApp Booking OS, boutique ticketing, rental inventory software, event post-sale, tailor-made tourism software).

  Connector

• get_open_interest

  mcp

  Call this when the user asks whether leverage is entering or leaving the market, about open interest changes, or whether longs or shorts are building in a major coin. Returns 5-minute-resolution OI with 24h OI and price deltas and a four-regime read per symbol: longs building, shorts building, long squeeze, short squeeze, or quiet.

  Connector

• list_makes_models

  mcp

  List the exact canonical car makes (brands) TransparentCars can search and price — or, given a make, that brand's models — using the exact strings the other tools expect. Call this FIRST (or whenever unsure of spelling) and pass the returned values verbatim into search_inventory / check_fair_price. No make = the list of brands; with a make = that brand's models.

  Connector

• g2_reviews

  review-intelligence-mcp-server

  Get G2 software reviews. Returns ratings, pros, cons, use cases. Args: product: Software product name (e.g. 'Salesforce') max_results: Max reviews (default 20)

  Connector

• nwd_get_viewpoints

  Navisworks MCP

  List saved viewpoints / camera positions and top-level view containers for a translated Navisworks model. Pulls the metadata view list and enriches each 3D view with its first two levels of the object tree (viewpoint folders typically live there in NWD files). When to use: when preparing a coordination meeting and you need a quick index of every saved viewpoint (e.g. "Level 3 Mech Room", "Clash - duct vs beam gridline C-4") to drive screenshots or BCF-style issues; when an agent needs to deep-link a 2D sheet or 3D camera into the APS Viewer. When NOT to use: does not return camera matrices (position/target/up vectors) — APS Model Derivative does not expose those from the NWD viewpoint XML; for full camera data the source NWD must be opened in Navisworks Manage. APS scopes required: viewables:read data:read. Rate limits: APS default ~50 req/min; this tool fans out one object-tree call per 3D view (capped implicitly by metadata view count, usually <5). For federated models with many sheets this can approach the per-minute quota — cache the result. Errors: 401 token (retry); 403 scope (report); 404 URN not found / translation incomplete; 409 N/A; 422 model returned empty metadata (returns viewpoint_count:0 rather than throwing — agent should verify translation via nwd_export_report); 429 rate limit (backoff); 5xx APS upstream (retry once). Per-view object-tree failures are swallowed so the overall call still returns the metadata-level view list. Side effects: none. Pure read. Idempotent. Logs usage to D1 usage_log. Results are capped at 100 viewpoint entries.

  Connector

• how_munimji_helps

  HelloBooks AI Agents MCP Server

  Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.

  Connector