"A tool or software for blending or 3D modeling" matching MCP tools:

• forecast_trend

  Moltline Data Desk

  Forecast future periods with a linear trend and honest fit quality. PREMIUM (license). For quick planning, not statistical modeling. Typical input {"values": [100, 120, 138, 161], "periods_ahead": 3} returns {"trend_per_period": 20.2, "r_squared": 0.998, "forecast": [180.9, 201.1, 221.3], "caveat": "..."}. Use when a series is roughly linear and fit quality matters as much as the projection. Not for seasonal or cyclical data, and not for measuring growth already observed (growth_rates). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "need at least 4 historical values"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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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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• colour_mix

  Colour Memory

  Simulate perceptually modelled subtractive mixing of two colours in CIE Lab space (not RGB screen blending). Returns the resulting mixed hex value and its nearest archive match with cultural context. Uses CIE Lab subtractive model for perceptual accuracy. Example: mixing Prussian Blue and Yellow Ochre gives a muted green — the tool identifies which archive colour that green most closely matches.

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• 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.

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• depwatch_preview

  Signal Oracle

  Free preview of breaking changes / new releases for a software dependency. Pass an npm/PyPI `package` (resolved and fetched live if not already tracked) or a GitHub `repo` (owner/repo). Returns up to 5 recent changes plus the package's current version. Full history, significance filtering, and the LLM brief are paid via x402.

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• find_seller

  VIA Network: Agentic Commerce

  Search the VIA network (VIA app + RRG + integrated platforms) for PRODUCTS and the sellers that offer them. Matches the published product catalogue (titles, descriptions, authors, categories) AND seller profiles, so 'books', an author, a title, or a category surfaces the actual product even when the seller's name does not contain the word. WHEN INTENT IS DEFINED: returns `results`, ONE relevance-ranked list blending every source. Each result carries a working `page_url` (the direct product page you give the user), plus `seller`, `price_usdc`, `image_url` (or null when the listing has no picture), and `mcp_ref` to transact. If more than one matches, PRESENT THEM side by side with prices and the key differences; do not silently pick one. WHEN INTENT IS LOOSE OR THERE IS NO MATCH: the response is `status: 'need_more_info'` with `suggested_dimensions`. DO NOT reply 'nothing is available'. Ask the user ONE clarifying question to sharpen intent (budget, brand/author, category, use), or retry this tool with a broader term, a synonym, the category, or the brand/author name. Only after a genuinely broadened retry also returns nothing should you say you could not find a match, and even then frame it as 'not found yet', not 'does not exist'.

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• forecast_trend

  Moltline Data Desk

  Forecast future periods with a linear trend and honest fit quality. PREMIUM (license). For quick planning, not statistical modeling. Typical input {"values": [100, 120, 138, 161], "periods_ahead": 3} returns {"trend_per_period": 20.2, "r_squared": 0.998, "forecast": [180.9, 201.1, 221.3], "caveat": "..."}. Use when a series is roughly linear and fit quality matters as much as the projection. Not for seasonal or cyclical data, and not for measuring growth already observed (growth_rates). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "need at least 4 historical values"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• colour_mix

  Colour Memory

  Simulate perceptually modelled subtractive mixing of two colours in CIE Lab space (not RGB screen blending). Returns the resulting mixed hex value and its nearest archive match with cultural context. Uses CIE Lab subtractive model for perceptual accuracy. Example: mixing Prussian Blue and Yellow Ochre gives a muted green — the tool identifies which archive colour that green most closely matches.

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• explain_three_primitives

  DiscreteRate

  Return a focused write-up of the three DRS modeling primitives: Constraint (rate-limiter), Buffer (accumulated state), Interrupt (stoppage). Use this when the user asks specifically about modeling primitives or how to spell a system in DRS. Deterministic text.

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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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• generate_3d_model

  Sats4AI - Bitcoin-Powered AI Tools

  Generate a textured 3D GLB model from EITHER a photo OR a text prompt (provide exactly one, not both). Uses Tencent Hunyuan3D — high-fidelity geometry and PBR materials. Async — returns requestId, poll with check_job_status. 1600 sats per model. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='generate_3d_model'.

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• 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).

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• tengu_ml_weights

  tengu-firm

  Current live ML ensemble voter weights (latest history row, broken out per market regime). Call it to know how the ensemble is blending its voters before interpreting a prediction; use tengu_ml_weights_history for drift over time.

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• pack_shipment

  3DPACK.ING — Container & Truck Load Planning

  Work out how a shipment fits into shipping containers, trucks or pallets, using a real 3D bin-packing solver. Describe the cargo in plain English -- quantities, dimensions, weights, and any constraints such as fragile, non-tiltable, max stack height or a preferred container type -- and get back which containers are needed, how full each one is, anything that did not fit, and a link to an interactive 3D load plan. Use this instead of estimating from volume. Volume arithmetic ignores stacking rules, orientation and weight limits, and overstates what fits by a wide margin on real cargo. For cargo that must not overhang -- drums, glass, anything that must stay level -- set `stability`. It is the one constraint the prompt cannot express, because it governs how the solver stacks rather than what is being shipped.

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• stock_quote

  fingers

  LIVE US stock/equity quote from Financial Modeling Prep, by ticker OR company name (e.g. 'AAPL' or 'Apple'): price, % change, market cap, exchange, day + 52-week range, volume. Use for any public-company / stock / ticker price question. This is the stocks equivalent of token_price — NOT for crypto tokens.

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• mesh-audit-external-posture

  MeshMarket

  Mesh Audit — External Posture — Consent-gated, READ-ONLY external posture report — informational only, not a formal audit or warranty. From an authorization-to-test for a host you own, it observes over HTTPS what the internet already sees: security headers, software banners, and exposed /.env //.git/admin surfaces. Always names what it did NOT check; internal targets refused. Input: {consent_id, asset} via /api/audit/consent. (6 MESH/call, a tool · audit)

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• 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)

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• create_deposit

  PikaSim

  Creates a payment invoice to add funds to the connected prepaid PikaSim Wallet, and returns a payment page link (the human can pay by card with 3D Secure, or crypto) plus per-coin crypto payment details. Accepts Bitcoin, Lightning, Monero, Zcash, USDT, and 50+ altcoins, or card on the payment page. Does not move funds itself; it returns an invoice the user pays externally, and the balance updates once the payment confirms. Use when check_balance is too low to purchase. Minimum deposit is $1 (no prepaid float required). A fully-autonomous agent holding crypto can pay WITHOUT a human: the tool returns native destinations for Lightning, Bitcoin on-chain, USDT (TRC-20/Tron), and Monero — pay the amount to any one of them. Crypto has no fee; card payments add a 2.9% processing fee on top of the credited amount. Balance is prepaid credit for purchases on PikaSim, never expires, and is not redeemable for cash. Requires a connected agent wallet (OAuth or ak_live_ key).

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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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• format_auto

  Caliper

  Auto-detect geometry file format and extract metadata statistics. Accepts a 3D geometry file via URL or base64 and returns structured metadata: bounding boxes, triangle counts, manifold analysis, point cloud statistics, and more. This is a read-only analysis tool — it does not perform mesh repair, format conversion, or boolean operations. Supported formats: STL, OBJ, PLY, PCD, LAS/LAZ, glTF/GLB. STEP and IGES support is planned. Provide either file_url (preferred for large files) or file_b64 (for files under 200KB). Include filename for format detection if using file_b64. When using file_url, the format is detected from the URL path extension; filename is not required. Files under 150KB are free. Larger files cost $0.02/MB via x402 (USDC on Base) or card via MPP (Stripe; adds $0.35 surcharge). If payment is required, the response includes payment details. Retry with the payment argument containing the payment proof. Privacy policy: https://caliper.fit/privacy

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