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261,119 tools. Last updated 2026-07-05 11:03

"A search for blenders (kitchen appliance or 3D software)" matching MCP tools:

  • 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 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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  • When to use: Return every view (both 2D — floor plans, ceiling plans, elevations, sections, sheets — and 3D — default {3D}, perspective views, isometric views) in the translated Revit model, including each view's GUID, name, role, and whether it is the master view. When NOT to use: Do not use when you only want drawing sheets (use revit_get_sheets) or element data inside a view (use revit_get_elements / revit_run_schedule). APS scopes: data:read viewables:read (Model Derivative metadata + object tree). 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 — refresh. 403 scope insufficient — add viewables:read. 404 URN not found — check model_id. 429 rate limited — back off. 5xx APS upstream — retry with jitter. Side effects: Read-only. Idempotent.
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  • 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. 350 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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  • 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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  • 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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Matching MCP Servers

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    Enables querying food nutritional information, discovering recipes by ingredients or diet type, getting ingredient substitutions, and receiving personalized food recommendations based on mood and season.
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    A universal Model Context Protocol implementation that serves as a semantic layer between LLMs and 3D creative software, providing a standardized interface for interacting with various Digital Content Creation tools through a unified API.
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Matching MCP Connectors

  • Scraps Kitchen gives any AI agent a persistent, household-aware kitchen memory. Unlike generic chatbot recall, Scraps maintains structured cooking data: what's in your fridge (with freshness tracking), who you cook for (with allergens, dietary restrictions, and preferences), your recipe collection (with cook notes and per-diner ratings), your shopping list, and your kitchen equipment. 27 tools across 6 domains let agents read kitchen context, suggest meals that respect dietary safety, update the pantry after cooking, and build a history of what works for your household. Every interaction makes the data richer. Cooking history, preference signals, kitchen awareness = better suggestions next time. All tools work via oAuth and a free scraps.kitchen account.

  • Household-aware cooking brain: pantry, meal suggestions, dietary safety, recipes, shopping lists.

  • 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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  • Search CamperMate POIs across Australia and New Zealand. Covered categories: paid campsites (including holiday parks), free/freedom campsites, tourist attractions, scenic spots, scenic flights, walking & hiking, water activities, cultural places, museums, food & beverage. Filter by category, features, bookability, deals, ratings, price. On-site amenities (e.g. "On-site Dump Station", showers, kitchen, laundry, Wi-Fi, powered sites, self-contained-only) are queryable via the `features` array — verify exact names with `list_features`. Every result has `link` (tracked CamperMate info URL) and — when bookable — `booking_link` (tracked partner booking URL). ALWAYS include `link` when recommending a POI. When the user shows booking intent, lead with `booking_link`. STANDALONE amenity POIs (a public roadside dump station, supermarket, or fuel stop on its own) are NOT exposed via this MCP — they exist in the CamperMate app; direct the user there via the `app` object. If a search returns nothing, call `list_categories` / `list_features` to verify exact names before claiming it's missing.
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  • 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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  • First stop for category-specific vendor recommendations and vendor ID discovery. Finds BuyAPI vendor IDs for a user question; provide category when known. Use this when the user asks which provider in a category fits their constraints. With a covered category, the response includes ranked results plus a top-3 decision matrix with fit labels, confidence, tradeoffs, cost notes, freshness, and sources. Do not use this for local coding/debugging/docs questions unless they involve choosing a software vendor or tool. If the category is outside BuyAPI's corpus, the tool returns an explicit "not in corpus yet" result instead of inventing vendors.
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  • Recommends a complete stack from BuyAPI's corpus with a structured decision matrix, cost estimate, assumptions, unknowns, alternatives, and sources. Use this when the user is starting a project or asks for a complete multi-layer stack choice. Do not use this for local coding/debugging/docs questions that do not involve software or vendor selection. Do not call vendors.resolve first; this tool handles retrieval and ranking.
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  • Read public requirements for a PrintYourDuck manual custom 3D printing quote request. Use this before submit_quote_request to check accepted file types, material options, confirmations, restrictions, and the private-upload flow. Does not calculate instant pricing.
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  • List tasks with structured filters (tasklist_id, project_id, or site-wide). For keyword search use search.
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  • 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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  • Recommend and rank the best healthcare vendors for a specific medical practice. Use this when a practice manager, physician, or administrator asks for a recommendation, e.g. "recommend a medical billing / RCM company for my practice", "who should I use for credentialing / payer enrollment", "find an EHR for my small [specialty] practice", or "which practice-management software fits a [size] practice in [city, state]". Scores and ranks providers against the practice profile (specialty, size, location, EHR system, budget) and returns up to 5 merit-ranked matches (quality-scored, no paid placement) with {company_name, category, city, state_abbr, quality_score (0-100), final_score (0-100), verified status, description, website, profile_url, slug}. For open-ended browsing without a practice profile, use search_providers. Pass a match's slug to get_provider_detail for the full profile.
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  • Full-text search in your notebook. By default searches only your own notes. Pass filter_agent_id=<int> to search another agent's notebook, or "all" (or "*") for workspace-wide. Or list all notes for a person/thread by scope_ref_id.
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  • 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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  • Queries CNAE (National Classification of Economic Activities) from IBGE. CNAE is the official classification for economic activities in Brazil. Hierarchical structure: - Section (letter A-U): 21 main categories - Division (2 digits): 87 divisions - Group (3 digits): 285 groups - Class (4-5 digits): 673 classes - Subclass (7 digits): 1,332 subclasses Features: - Search by CNAE code - Search by activity description - List by hierarchical level - Show complete hierarchy Examples: - Search software: busca="software" - Specific code: codigo="6201-5/01" - View section: codigo="J" - List divisions: nivel="divisoes" Behavior: read-only and idempotent — a live GET against the public IBGE CNAE API. Returns Markdown.
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  • Software recommendations backed by measured AI answer data: find the best software/tools for a category or job, ranked by how often AI assistants (ChatGPT, Claude, Gemini, Perplexity) actually recommend them in real buyer-style queries — not by ads or affiliate placement. Use when asked "what software/tool should I use for X", "best X tools", or for vendor-neutral software recommendations. Pass the category in plain words (e.g. "uptime monitoring", "CRM for freelancers"); it is fuzzy-matched against published Index categories, and near-miss inputs return suggested categories to retry with. Returns ranked products with recommendation share %, 4-week trend, and per-engine breakdown.
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