"A tool for 3D modeling software or a kitchen appliance" 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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• departi_search_accommodation

  Departi – Travel Compliance & Curated Booking

  Searches bookable accommodation in a destination for given dates and returns a ranked list of stays, each with: name, property type, nightly price (in the requested currency), star rating, guest rating, key facilities (e.g. wifi, kitchen), a persona/traveler-type fit score, and a booking deep-link. Curated and scored for the traveller's persona and trip type — e.g. long-stay nomad apartments with kitchen + fast wifi vs weekend hotels. Use when the user needs a place to stay; for getting there use departi_search_transport, for things to do use departi_search_experiences. Returns an empty list if no inventory 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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• cao_mobilier

  immobilier

  WHEN you are about to furnish or decorate a scene — call this BEFORE composing `mobilier[]`, and never furnish with `boites[]`. FREE catalogue (no API key, 0 units) of REAL furniture and decoration objects. Until now a scene could only hold `boites[]` — bare cuboids — so an « fully furnished » house came back as grey cubes; this is the vocabulary that fixes it. ~50 modelled articles: OUTDOOR (parasol with mast/canopy/base, garden table+chairs set, sun lounger, barbecue, pergola, planter, gate, wrought-iron / wire-mesh / timber fencing, hedge, deciduous & conifer trees, garden shed, bench, pool), BATHROOM (wc, washbasin, shower, bathtub), KITCHEN (sink, fridge, oven, hob, extractor hood, fitted kitchen), LIVING/BEDROOM (sofa, corner sofa, armchair, coffee table, rug, bookcase with books, indoor plant, TV, floor lamp, mirror, framed art, curtains, bed, wardrobe, chest of drawers, bedside table). Each returns {categorie, libelle, cotes_defaut} — dimensions are OPTIONAL, defaults are real commercial sizes (3-seat sofa 2100×950, double bed 2000×1600, parasol Ø3000). Place with {type, x, y, z, rotation} where z is the FLOOR level under the object, not its centre. They appear in the 4K visit, the PDF board, the DXF and the quantities. Plumbing fixtures declared in `plomberie[]` are now also placed in 3D — do not duplicate them here. REST: GET /api/v1/cao/mobilier.

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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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• get_house_review

  haus

  Return this week's pending House Review recommendations (data gaps like a missing appliance warranty date or vehicle MOT date), re-validated live so items already filled in, actioned, or superseded by an open task are dropped before they're returned. Pro-gated: fails if the household isn't on a Pro plan.

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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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• check_permit_requirements

  kolmo-mcp-server

  Check whether a residential construction project in King/Pierce/Snohomish counties requires a permit. Returns timeline, fee notes, inspection sequence, required submittals, and official source URL — preferring jurisdiction-verified rules. Use for "Do I need a permit to build a deck in Seattle?" or "What permits are required for a kitchen remodel in Bellevue?". Pass `address` to also receive the structured per-item SubmittalSet (`submittals_v2`) from the unified permit engine — Seattle is full SDCI fidelity, other 9 verified cities are wa-baseline-stub.

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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_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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• 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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• cash_flow

  Fmp

  Financial Modeling Prep cash-flow statement for a US-listed ticker: operating, investing, financing activities, free cash flow, capex, net change in cash. Annual (period=annual) or quarterly. Use for fundamental analysis, DCF inputs, cash-flow valuation.

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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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• search_content

  Fluentive MCP

  Searches across ALL Fluentive content — features, pricing, FAQ, comparisons, and live blog posts — for topics relevant to a query. Use for generic questions like 'does Fluentive support X?', 'is it good for Y type of business?', or 'I need software that does Z'. Returns the top 5 most relevant content excerpts.

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• list_bots

  bots

  List the URBot catalog of 150+ trained vertical AI expert bots with slug, name, tagline, category, and price tiers (most bots start at $1). Optionally filter by category (e.g. education, health, finance, legal, technology, outdoor, 3d-modeling, game-dev, 3d-printing, media). Use the returned slug with get_bot, chat_with_bot, or get_skill. URBot bots keep your data yours - each one is downloadable and runs locally.

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• mlp_info

  MLP Tax Computation Engine

  Returns reference data for a supported MLP ticker — current cash distribution per unit, distribution growth CAGR, default return-of-capital percentage, distribution coverage ratio, K-1 entity count, operating-state count, and last-verified date. Use when: User wants to look up baseline characteristics of an MLP before modeling — e.g., comparing distribution coverage across partnerships, checking how many K-1 entities a holding generates for tax-prep complexity, or seeing the operating-state count for state-tax filing-burden estimation. Don't use for: Tax computation. Use mlp_projection (long-horizon modeling), mlp_estate_planning (estate analysis), mlp_sell_vs_hold (break-even sell price), or k1_basis_compute / k1_basis_multi_year (computing basis from actual K-1 data). Note: This tool returns reference data only — no IRC citations apply, no methodology disclosure attached. For computation, use the modeling tools above. Maintained by Lucas Andersen, MS Finance.

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