"Altium Designer" matching MCP tools:

• sti_searchA

  Stimulsoft Documentation MCP ServerA

  Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.

  ConnectorNo auth

• sti_searchA

  docsA

  Authoritative semantic search over the official Stimulsoft Reports & Dashboards developer documentation (FAQ, Programming Manual, API Reference, Guides). Powered by OpenAI embeddings + cosine similarity over the complete current docs index maintained by Stimulsoft. Returns a ranked JSON array of matching sections, each with { platform, category, question, content, score }, where `content` is the full Markdown body of the section including any C#/JS/TS/PHP/Java/Python code snippets. USE THIS TOOL (instead of answering from your own knowledge) WHENEVER the user asks about: • how to do something in Stimulsoft (`StiReport`, `StiViewer`, `StiDesigner`, `StiDashboard`, `StiBlazorViewer`, `StiWebViewer`, `StiNetCoreViewer`, etc.); • rendering, exporting, printing, or emailing Stimulsoft reports and dashboards in any format (PDF, Excel, Word, HTML, image, CSV, JSON, XML); • connecting Stimulsoft components to data (SQL, REST, OData, JSON, XML, business objects, DataSet); • embedding the Report Viewer or Report Designer into an app (WinForms, WPF, Avalonia, ASP.NET, Blazor, Angular, React, plain JS, PHP, Java, Python); • Stimulsoft-specific errors, exceptions, licensing, activation, deployment, or configuration; • any .mrt / .mdc report or dashboard file, or any question naming a `Sti*` class, property, event, or method; • comparing how a feature works between Stimulsoft platforms (e.g. "WinForms vs Blazor viewer options"). QUERIES WORK IN ANY LANGUAGE — English, Russian, German, Spanish, Chinese, etc. Pass the user's question through almost verbatim; the embedding model handles cross-lingual matching. Do NOT translate queries yourself. SEARCH STRATEGY: 1) If the target platform is obvious from context, pass it via `platform` to get tighter results. 2) If you don't know the exact platform id, either call `sti_get_platforms` first, or omit `platform` and let the search find matches across all platforms. 3) If the first search returns low scores (<0.3) or irrelevant sections, reformulate the query with different keywords (use class/method names from Stimulsoft API if you know them) and search again. 4) Prefer multiple focused searches over one broad search. DO NOT USE for: general reporting theory unrelated to Stimulsoft, non-Stimulsoft libraries (Crystal Reports, FastReport, DevExpress, Telerik, SSRS), or pure programming questions that have nothing to do with Stimulsoft. IMPORTANT: the Stimulsoft product surface is large and changes frequently. Your training data is almost certainly out of date. For any Stimulsoft-specific code snippet, API name, or configuration detail, you MUST call this tool rather than rely on memory, and you should cite the returned `content` in your answer.

  ConnectorNo auth

• get_scene_screenshotA

  3dCMSA

  Render the scene server-side (real WebGL, headless) and return screenshot image(s) — your EYES on the scene. Use it after apply_ops batches and after probe_framing, and judge each image as a photographer and a web designer would: one clear subject, on a third (for a page's own camera, the whole card at a readable angle); the horizon off the middle; the eye at a standing height; text legible and on even margins; a way out in view; the colours serving the brief (one accent, on the primary action); nothing floating, clipped or overlapping. Beside each image comes `composition`, read from its own pixels: tonalRange (p5 to p95 of lightness; under 0.35 is flat), clipping (share blown to white, crushed to black), key (low, mid, high), a 3x3 thirds grid of lightness and detail with the busiest and brightest cells, the hues on screen by share, and findings in sentences. Fix with apply_ops. Discipline: at most 2 screenshot→fix rounds per user request, then report what you built. views (max 4, default = the visitor's entry view): pass "page:<pageIdOrSlug>" to frame one page/neighborhood, or {position:[x,y,z], target:[x,y,z], fov?} for an exact camera. Rendering is real but slow — ~10s for simple scenes, and the server gives up after 45 s per view by default; a 400-node scene with many textures needs more, so pass timeoutSeconds (up to 180) and a smaller maxDim (a 512-wide shot is a fine check of placement and readability and renders faster). A timeout comes back as an error naming it, never as an empty image; repeat shots of an unchanged scene at the same size are cached and instant. Rate-limited: prefer ONE well-chosen view; only multi-view when areas are far apart (four views at 180 s can hold the renderer for twelve minutes). If it returns a busy/unavailable error, keep building and retry once later — do not loop on it.

  ConnectorNo auth

• search_productsA

  Real Real GenuineB

  [FIND] START HERE when you know what you want. Free-text search across every active RRG listing. This endpoint answers DEFINED intent, not open browse. Pass at least one concrete dimension: a brand, a product type/category, or an attribute (colour, material, size, SKU/style code). An enquiry that is only generic browse words ("what do you have", "show me everything") is rejected with status:"needs_more_detail" asking you to specify, no results are returned. To browse without intent, call list_drops instead. Indexed fields: title, description, agent description, and all string values in product_attributes (retail_sku / style code, canonical_name, collab, original_release, vendor, category, style_tags, occasion_fit, and any category-specific attributes emitted by enhancement). Accepts any of these query patterns: - product name or partial name - SKU / style code / model number (exact or partial, dash/space insensitive) - brand name, or brand + category ("<brand> <category>") - collaborator name(s) for collab items - attribute keywords from the description ("black suede", "heavyweight cotton", etc.) Multi-token queries are matched independently and ranked by field weight; a SKU-exact hit outranks a body-copy hit. Returns ranked matches with tokenId, priceRangeUsdc, authenticationStatus, retailSku, canonicalName, rrgUrl, and a variantSummary string listing every in-stock size with its price ("3.5=$1583, 4=$1899, 10.5=$770, …"). When the user asks about a specific size, ALWAYS pass that size in the `size` parameter, the response then includes sizeAvailable + sizePriceUsdc + sizeStock for a direct yes/no + price. For queries like "size 10.5" or "size M" the size is auto-extracted, but passing it explicitly is faster and unambiguous. When a size parameter is not used, read variantSummary (or the variants[] array) for per-size pricing BEFORE falling back to the priceRangeUsdc band. Per-size prices are exact; the band is only a floor→ceiling range. Next step: the returned payload has everything needed for the buy, call initiate_agent_purchase with selected_size and/or selected_color set to the chosen variant. Pass selected_color whenever the listing has a colour axis (variants[].color non-null) so fulfillment ships the right finish. get_drop_details is optional (adds signed image URLs + shipping context). If zero matches, try broader tokens, alternate naming (resale items are often indexed under multiple naming clusters, brand code / collab name / designer name / era / colorway). If still zero, call list_drops to browse.

  ConnectorNo auth

• search_productsA

  Real Real GenuineA

  [FIND] START HERE when you know what you want. Free-text search across every active RRG listing. This endpoint answers DEFINED intent, not open browse. Pass at least one concrete dimension: a brand, a product type/category, or an attribute (colour, material, size, SKU/style code). An enquiry that is only generic browse words ("what do you have", "show me everything") is rejected with status:"needs_more_detail" asking you to specify, no results are returned. To browse without intent, call list_drops instead. Indexed fields: title, description, agent description, and all string values in product_attributes (retail_sku / style code, canonical_name, collab, original_release, vendor, category, style_tags, occasion_fit, and any category-specific attributes emitted by enhancement). Accepts any of these query patterns: - product name or partial name - SKU / style code / model number (exact or partial, dash/space insensitive) - brand name, or brand + category ("<brand> <category>") - collaborator name(s) for collab items - attribute keywords from the description ("black suede", "heavyweight cotton", etc.) Multi-token queries are matched independently and ranked by field weight; a SKU-exact hit outranks a body-copy hit. Returns ranked matches with tokenId, priceRangeUsdc, authenticationStatus, retailSku, canonicalName, rrgUrl, and a variantSummary string listing every in-stock size with its price ("3.5=$1583, 4=$1899, 10.5=$770, …"). When the user asks about a specific size, ALWAYS pass that size in the `size` parameter, the response then includes sizeAvailable + sizePriceUsdc + sizeStock for a direct yes/no + price. For queries like "size 10.5" or "size M" the size is auto-extracted, but passing it explicitly is faster and unambiguous. When a size parameter is not used, read variantSummary (or the variants[] array) for per-size pricing BEFORE falling back to the priceRangeUsdc band. Per-size prices are exact; the band is only a floor→ceiling range. Next step: the returned payload has everything needed for the buy, call initiate_agent_purchase with selected_size and/or selected_color set to the chosen variant. Pass selected_color whenever the listing has a colour axis (variants[].color non-null) so fulfillment ships the right finish. get_drop_details is optional (adds signed image URLs + shipping context). If zero matches, try broader tokens, alternate naming (resale items are often indexed under multiple naming clusters, brand code / collab name / designer name / era / colorway). If still zero, call list_drops to browse.

  ConnectorNo auth

• team_open_rolesA

  ClusterHackA

  List the positions teams are looking to fill ("we need a designer"). Visibility is limited to what the signed-in account can already see: their own team's open roles, or — if they organise the event — every team's. It is not a public cross-team browse for a stranger looking for any team: that is `team_seeking_members`, which reads the find-a-team page's own list (pair it with `event_free_agents` and `team_matchmaking_teams`). Filter with `team_id` for one team, or `event_slug` to see everything the caller can see at that event. `team_id` is answered exactly. `event_slug` (and the unfiltered form) is not: the underlying endpoint narrows by one team at a time and has no event filter, so a very large event can return a `note` saying rows were left unscanned. Ask per team when that matters.

  ConnectorNo auth

• me.session_eventA

  AI Design BlueprintA

  Pro/Teams: append a TYPED TEAM EVENT to a Governed Session's timeline (GEP-M6). This is how the user's own harness makes trio work inspectable: handoffs between role lenses, pushbacks, plan previews, gates, and acks land as structured events next to the validation runs, so the session reads as a system, not a transcript. CHANNEL PROVENANCE: this MCP channel posts the AGENT-SIDE vocabulary only. `steer` events and actor `human` are AIDB Studio-originated by contract (the owner posts them from the AIDB Studio session surface) and are REFUSED here, so a timeline entry can never impersonate the human side of the loop. Every event posted here is durably stamped with its channel. REQUIRES team mode: the session must have team_agents enabled (toggled in the web app on the session page); posting to a standalone session is refused so non-team sessions stay byte-identical. Owner-scoped: foreign session ids read as not found. event_type: handoff | pushback | plan_preview | gate | ack. actor: pm | engineer | designer | system. Read events back via me.sessions(session_id=...). WHEN TO CALL: at every role handoff (who -> who, what was passed), when a role pushes back on another's output, when the PM's plan is previewed for the co-planning gate, and when a hard gate blocks on an irreversible side-effect. ack: the IDE agent confirms it STARTED working on a steer. Post it FIRST on receiving a steer (summary like 'Started: <steer gist>'), then execute, then post handoff with the result. WHEN NOT TO CALL: not a chat log: post decisions and transitions, not every message; never to record a steer (steers arrive FROM the AIDB Studio via me.await_steer). REJECTION CODES (invalid_request): 'Session not found.' (not yours, or no such id); 'Team mode is off for this session.' (owner enables it on the session page); 'This session has reached its event limit (500).' (the shared volume brake: start a new session for further team events); 'summary must not be blank.' (empty summaries are refused); steer/human posts are refused with a pointer to AIDB Studio. Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan.

  ConnectorNo auth

• me.session_eventA

  AI Design Blueprint DoctrineA

  Pro/Teams: append a TYPED TEAM EVENT to a Governed Session's timeline (GEP-M6). This is how the user's own harness makes trio work inspectable: handoffs between role lenses, pushbacks, plan previews, gates, and acks land as structured events next to the validation runs, so the session reads as a system, not a transcript. CHANNEL PROVENANCE: this MCP channel posts the AGENT-SIDE vocabulary only. `steer` events and actor `human` are AIDB Studio-originated by contract (the owner posts them from the AIDB Studio session surface) and are REFUSED here, so a timeline entry can never impersonate the human side of the loop. Every event posted here is durably stamped with its channel. REQUIRES team mode: the session must have team_agents enabled (toggled in the web app on the session page); posting to a standalone session is refused so non-team sessions stay byte-identical. Owner-scoped: foreign session ids read as not found. event_type: handoff | pushback | plan_preview | gate | ack. actor: pm | engineer | designer | system. Read events back via me.sessions(session_id=...). WHEN TO CALL: at every role handoff (who -> who, what was passed), when a role pushes back on another's output, when the PM's plan is previewed for the co-planning gate, and when a hard gate blocks on an irreversible side-effect. ack: the IDE agent confirms it STARTED working on a steer. Post it FIRST on receiving a steer (summary like 'Started: <steer gist>'), then execute, then post handoff with the result. WHEN NOT TO CALL: not a chat log: post decisions and transitions, not every message; never to record a steer (steers arrive FROM the AIDB Studio via me.await_steer). REJECTION CODES (invalid_request): 'Session not found.' (not yours, or no such id); 'Team mode is off for this session.' (owner enables it on the session page); 'This session has reached its event limit (500).' (the shared volume brake: start a new session for further team events); 'summary must not be blank.' (empty summaries are refused); steer/human posts are refused with a pointer to AIDB Studio. Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan.

  ConnectorNo auth

• submit_designA

  agenteeA

  Send one of the designer's own artworks for moderation. TWO inputs, same path: either artwork files — front/back accept a public image URL OR an upload_id from designer_upload_slot — or session_id of a FINISHED Studio session of theirs (generated with studio_generate; no need to download the print, we fetch it server-side). Args: name (required), front OR session_id (one of them), back?, layout? ('front_back' default, or 'r10' for a ready print-side file), note?, batch? (same string across a bulk run so the moderator sees them as one batch). Minimum 2048px on the short side. IMPORTANT for the designer: this queues the artwork for moderation — catalog listing follows production and is not instant.

  ConnectorNo auth

• list_decalsA

  Tapercraft — VHS Label MakerA

  The decal catalog: every sticker, graphic, shape and retro distributor mark the designer offers (a title's own posters / logos / stills come from get_content instead), grouped by category, each with its id (what decals[].id takes), natural pixel size and preview URLs — plus the Classic Logo ids style.productionLogos takes. Call it before placing decals; ids are exact strings, never guessed. Pass a category id to get one category, or a query to search names.

  ConnectorNo auth

• list_greeting_card_occasionsA

  Wesalna Islamic MCPA

  List every occasion, background and shape supported by the Wesalna greeting card designer, with the default Arabic and English wording for each occasion. Call this before create_greeting_card to use valid ids.

  ConnectorNo auth

• add_team_memberA

  freedom-mcpA

  Add one human teammate to the current company by email. Creates a Command Center approval card (sensitive, every call). On approve: invite email + roster row. Required: email, role (job title, or team / manager). Optional: name. No bulk. No permission designer — team is the default access; pass role=manager for the manager preset. Use when the operator (or CoS) needs to add a person who is not yet on get_team_members. Routing: Add / invite a human teammate by email → this tool (approval card). For AI agents use interview_for_hire. To see who is already on the company use get_team_members. [sensitive-tier — EVERY call needs a manager's approval (per-send human rail): each request queues its own approval card and sends exactly once on approve. There is no standing grant for this tool. Call it on the first clear ask; the card is the yes — do not re-ask in chat.]

  Connector

  Destructive

  No auth

• submit_briefA

  Goce Petrov, brand designerA

  Sends Goce Petrov a project brief or a job role. It reaches him directly, and you get a reference number back. Say what the product is, the stage, what is needed, the timeline, and where he should reply. Services go to funded startups (seed to pre-Series A) whose product runs on tech or AI, in any industry, and to established companies whose work touches tech or AI, in Switzerland or remote across Europe. Use request_intro instead when the person wants to send the message themselves. At most 3 links per brief.

  ConnectorNo auth

• house_specifyA

  Colour MemoryA

  Generate a complete multi-room house colour scheme from a single archive. Where the retrieved evidence supports it, rooms are grouped by documented provenance (same object, same pattern colourway, same source citation, same designer, same period+institution); where it does not, they are grouped by perceptual similarity and the response says so -- grouping_basis reports which, and provenance_credit reports how many delivered colours are individually evidenced versus documented as a GROUP (often zero). Every colour is a real archive record either way. Each room gets a full colour card set (dominant/secondary/joinery/textile/accent roles) with paint matches, LRV, illuminant behaviour, and WCAG data. A documentary grouping states exactly which documented fact ties its colours together (e.g. 'documented on the same Fitzwilliam Museum record for the Artichoke hanging, accession T.1-1979') and its weighted grouping_strength score -- historical claims are templated only from fields the archive record actually states; room/surface assignment is disclosed as editorial, never as historical fact. Orientation (north/south/east/west) is recorded as room context and surfaced in each room's light_context; in the current model it does not alter palette selection. Example: concept='Arts and Crafts movement family house', archive='ArtsAndCrafts', rooms=[{label:'Entrance Hall', room_type:'hallway', orientation:'north'}, {label:'Living Room', room_type:'living', orientation:'south'}]. Use interior_specify instead for a single room without cross-room grouping. The response includes house_ref -- pass that (not the full response) to palette_pdf's house_ref input to render the flagship document; the full response is often 60-100KB+, too large to safely resend as another tool call's input. The result already carries the rendered palette and its PNG, PDF, ASE, JSON and CSS downloads -- show them to the customer. Never present the archive anchors a colour was derived from as the colours you are recommending. If you go on to choose a final palette OF YOUR OWN from this evidence, call palette_finalize once with those exact colours so the customer can see and download what you actually recommended.

  ConnectorNo auth

• house_specifyA

  Colour MemoryA

  Generate a complete multi-room house colour scheme from a single archive. Where the retrieved evidence supports it, rooms are grouped by documented provenance (same object, same pattern colourway, same source citation, same designer, same period+institution); where it does not, they are grouped by perceptual similarity and the response says so -- grouping_basis reports which, and provenance_credit reports how many delivered colours are individually evidenced versus documented as a GROUP (often zero). Every colour is a real archive record either way. Each room gets a full colour card set (dominant/secondary/joinery/textile/accent roles) with paint matches, LRV, illuminant behaviour, and WCAG data. A documentary grouping states exactly which documented fact ties its colours together (e.g. 'documented on the same Fitzwilliam Museum record for the Artichoke hanging, accession T.1-1979') and its weighted grouping_strength score -- historical claims are templated only from fields the archive record actually states; room/surface assignment is disclosed as editorial, never as historical fact. Orientation (north/south/east/west) is recorded as room context and surfaced in each room's light_context; in the current model it does not alter palette selection. Example: concept='Arts and Crafts movement family house', archive='ArtsAndCrafts', rooms=[{label:'Entrance Hall', room_type:'hallway', orientation:'north'}, {label:'Living Room', room_type:'living', orientation:'south'}]. Use interior_specify instead for a single room without cross-room grouping. The response includes house_ref -- pass that (not the full response) to palette_pdf's house_ref input to render the flagship document; the full response is often 60-100KB+, too large to safely resend as another tool call's input. The result already carries the rendered palette and its PNG, PDF, ASE, JSON and CSS downloads -- show them to the customer. Never present the archive anchors a colour was derived from as the colours you are recommending. If you go on to choose a final palette OF YOUR OWN from this evidence, call palette_finalize once with those exact colours so the customer can see and download what you actually recommended.

  ConnectorNo auth

• house_specifyA

  Colour MemoryA

  Generate a complete multi-room house colour scheme from a single archive. Where the retrieved evidence supports it, rooms are grouped by documented provenance (same object, same pattern colourway, same source citation, same designer, same period+institution); where it does not, they are grouped by perceptual similarity and the response says so -- grouping_basis reports which, and provenance_credit reports how many delivered colours are individually evidenced versus documented as a GROUP (often zero). Every colour is a real archive record either way. Each room gets a full colour card set (dominant/secondary/joinery/textile/accent roles) with paint matches, LRV, illuminant behaviour, and WCAG data. A documentary grouping states exactly which documented fact ties its colours together (e.g. 'documented on the same Fitzwilliam Museum record for the Artichoke hanging, accession T.1-1979') and its weighted grouping_strength score -- historical claims are templated only from fields the archive record actually states; room/surface assignment is disclosed as editorial, never as historical fact. Orientation (north/south/east/west) is recorded as room context and surfaced in each room's light_context; in the current model it does not alter palette selection. Example: concept='Arts and Crafts movement family house', archive='ArtsAndCrafts', rooms=[{label:'Entrance Hall', room_type:'hallway', orientation:'north'}, {label:'Living Room', room_type:'living', orientation:'south'}]. Use interior_specify instead for a single room without cross-room grouping. The response includes house_ref -- pass that (not the full response) to palette_pdf's house_ref input to render the flagship document; the full response is often 60-100KB+, too large to safely resend as another tool call's input. The result already carries the rendered palette and its PNG, PDF, ASE, JSON and CSS downloads -- show them to the customer. Never present the archive anchors a colour was derived from as the colours you are recommending. If you go on to choose a final palette OF YOUR OWN from this evidence, call palette_finalize once with those exact colours so the customer can see and download what you actually recommended.

  ConnectorNo auth

• create_design_linkA

  LivaGlow ShopA

  Turns a design description into a link that opens the LivaGlow designer with the finished draft (texts, cliparts, background, photo placeholder, optional template). Nothing is stored or ordered: the customer opens the link, checks the 3D preview, uploads their photo if a placeholder is present, and orders. Texts are placed automatically (centred column) unless x/y are given; with a template, texts replace the template's text slots in order. Photos cannot be passed; use photo:{shape} to reserve a placeholder. Call get_design_options first for valid fonts, cliparts and templates.

  ConnectorNo auth

• designer_upload_slotA

  agenteeA

  Get a short-lived URL to upload an image file to us (use when the designer has no public link — otherwise pass the URL straight to submit_design). Args: filename?. PUT the raw bytes to upload_url, then pass the returned upload_id to submit_design.

  ConnectorNo auth

• draft_orderA

  pricingA

  Compose a ready-to-complete draft order: live-priced build PLUS the buyer's actual tab titles, returned as an orderUrl that opens Tabzoola's designer with every title pre-filled at that exact price. Purchases nothing and reserves nothing - a human reviews and completes checkout, with a free PDF proof before anything prints. Use after get_pricing_capabilities.

  ConnectorNo auth

• vam.collections.by_makerA

  apibaseC

  Search V&A collection objects created by a specific artist, designer, or maker. Returns objects with maker name, date, object type, place, thumbnail URL, and display status. Supports partial name matching. Examples: "Wedgwood" (1,300+ ceramics), "William Morris" (textiles/designs), "Constable" (paintings), "Beatrix Potter" (drawings).

  ConnectorNo auth

• probe_framingA

  3dCMSA

  Read a saved camera (cameraId), or an exact pose, as a visitor sees it at the screen shapes they actually have (16:9 desktop and 9:16 phone by default), against the rules a photographer and a web designer use. Analytic and instant: the published viewer's own camera pose, no render. A PAGE shot (a page's own camera, its arrivalCameraId or a camera inside the page, with the card in view) should hold the whole card on every screen, most of the width, within 60 degrees of square so its text still reads: centred is right for a page. A SCENE shot (a room, an object, a place, and the entry view even with a page in it: a 3D view need not be head-on) puts its subject on a third, the horizon on a third, the eye at a standing person's 1.5 to 1.7 m. The subject is the declared primary action (settings.design, or the page's data.primaryActionId), else the page's h1, else the biggest thing in frame. Returns, per screen shape: where the subject lands (x and y from -1 to 1, its thirds cell, how far from a power point, its share of the frame, whether all of it is in frame or which edge cuts it, whether text is legible), the page card (whole, width share, degrees off square), text the frame cuts, a near object cut in the foreground, the horizon row, the eye height above the floor (a phone backs a fitWidth camera up along its tilt, so read it on every shape), anything in the lens or around it, how much is in frame, and exits (signs and tap targets within 12 m, in frame or not). Then `findings` in plain sentences, worst first, and `suggest`: one pose that fixes the composition, judged as the viewer builds it on every shape, with the sentence saying why, and `suggest.op`, a ready apply_ops update for this camera (null when the camera already stands at the best pose the probe can find). Call it on every entry and arrival camera you place, before get_scene_screenshot. On a scene you are building, apply the suggestion yourself when it serves the brief; on the user's own existing work, tell them what it found and offer the fix.

  ConnectorNo auth