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

• query.product

  DezignWorks Product Oracle

  Query the DezignWorks knowledge base for information about the product, troubleshooting, features, workflows, supported hardware, and licensing. DezignWorks is reverse engineering software that integrates with SolidWorks and Autodesk Inventor, converting 3D scan data and probe measurements into parametric CAD models. Use this tool when answering questions about the product's capabilities, compatibility, or how to accomplish specific tasks.

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• 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).

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• 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.

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• 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.

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• list_models

  ScanBIM MCP

  List every object currently stored in the scanbim-models OSS bucket, with URN, size in MB, and a viewer URL for each. Returns the raw OSS inventory, not the D1 models table, so freshly uploaded items appear immediately. When to use: you need to enumerate previously uploaded models to find a URN, show an inventory, or pick one for a follow-up tool call. When NOT to use: you already know the exact URN — call get_model_metadata directly. This tool is not a search; it returns up to the OSS default page (typically first 10 objects unless OSS paginates). APS scopes: bucket:read data: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 bucket not found — no models have been uploaded yet (upload one first); 429 rate limited — backoff and retry; 5xx APS upstream outage — retry with jitter. Side effects: READ-ONLY. Idempotent.

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• archive_coverage_gap

  Colour Memory

  Given a list of themes, report which are well-evidenced in the archive and which are under-evidenced or missing. Returns a coverage matrix: for each theme, entries found, coverage grade (strong/moderate/weak/missing), best match with claim strength, and what source type would be needed to improve coverage. Use this BEFORE building an archive_report_brief or brief_forensic to know where the evidence is strong and where gaps will appear. Prevents building beautiful reports that quietly ignore half the brief.

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• archive_coverage_gap

  Colour Memory

  Given a list of themes, report which are well-evidenced in the archive and which are under-evidenced or missing. Returns a coverage matrix: for each theme, entries found, coverage grade (strong/moderate/weak/missing), best match with claim strength, and what source type would be needed to improve coverage. Use this BEFORE building an archive_report_brief or brief_forensic to know where the evidence is strong and where gaps will appear. Prevents building beautiful reports that quietly ignore half the brief.

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• govcon_search_contract_awards

  DataNexus MCP

  Search government contract awards by keyword, agency, and date range. keyword: Contract scope e.g. "cybersecurity software". agency: Awarding agency e.g. "Department of Defense". Optional. date_from: Earliest award date ISO 8601 e.g. "2024-01-31". Optional. jurisdiction: "US", "EU", or "UK". Default "US". Returns: award amounts, recipient vendors, NAICS codes, award dates. Use govcon_fetch_vendor_contract_history for all contracts by a specific vendor. Use govcon_fetch_open_solicitations for active bids, not past awards. Source: USASpending.gov + SAM.gov. 4-hour cache. Example: search_contract_awards(keyword="cybersecurity software", agency="Department of Defense")

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• get_building_breakdown

  Cenogram - Polish Real Estate Data

  Get the building-by-building breakdown for one transaction: footprint area, number of storeys, and estimated total floor area (footprint × storeys) for each building on the property. search_transactions / search_by_area / search_by_polygon return per-transaction building SUMS inline; this tool splits them into individual buildings. Use it after a search when a result has building data and you need the detail (e.g. a developed-land deed covering several buildings). The transaction_id is the id shown on a search result that has building data. Cost: 1 token. Returns nothing for a transaction with no buildings.

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• 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.

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• 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.

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• 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.

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• get_investment_category_signal

  Stratalize Healthcare

  Use when evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision. Returns growth signal, top brands, and citation evidence for any software category. Example: AI infrastructure category — GROWTH signal, top brands Nvidia 67% citation share, Anthropic 18%, xAI 9% — accelerating citation growth signals sustained investment thesis. Source: Stratalize citation heuristics.

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• get_tech_stack

  Makuri Showcase (CogniLedger)

  Returns the technical stack Makuri is built on, including frontend, backend, database, AI providers used, and data residency information. Use when the user asks how Makuri is built or which AI models it uses. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.

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• shakespeare_insult_generate

  Shakespeare Insult

  Generate a Shakespearean insult; optionally target a specific person or recipient category (colleague/ex/traffic/software/abstract_concept/the_universe), set severity (mild→nuclear), and request a modern English translation alongside the original.

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• cite

  dynamic-feed

  Get a fresh, CITEABLE source + timestamp for a current datapoint — so you can cite it, not guess. Pass ANY tool, source, or topic (earthquakes, current_weather, USGS, Open-Meteo, …) for its authoritative source + licence + attribution + verify URL, or a software product (python, nodejs, …) for its live latest-version citation. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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• fx_list_currencies

  exchange-rates-mcp-server

  List all supported ISO 4217 currency codes with their full names. Call this before converting to disambiguate "dollars" (USD vs AUD vs CAD vs HKD vs SGD) or to validate a user-supplied currency code. Covers the ~30 ECB reference currencies.

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• 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.

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• 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.

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• 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.

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