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459,989 tools. Updated 2026-08-17 12:01

"A resource for skinning and rigging 3D models" matching MCP tools:

  • Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clients without MCP resource support, e.g. Gemini); `signal:{market}:{symbol}` returns the symbol's latest combined research signal. Triggers: ChatGPT connectors / Deep Research call this after `search`. Clients without MCP resource support can call it directly with a known resource id, e.g. fetch("resource:market://global/summary"). When to call: whenever the full content behind a search result id is needed. Prerequisites: a valid id — from `search` results or a known namespace id. Next steps: for tool docs, call the named tool via tools/call; for signals, get_signal_detail / explain_decision for deeper evidence. Caveats: uncovered resource uris return description-only text (no fabricated data). `text` is a JSON document for resource/signal ids. Output: {id, title, text, url, metadata, disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape. Args: id: document id — "tool:{name}", "resource:{uri}", or "signal:{market}:{symbol}" (market: crypto / kr_stock / us_stock) Disclaimer: Information only, not investment advice.
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  • Long-range climate projections from bias-corrected daily CMIP6 models, covering 1950-01-01 to 2050-12-31 at any coordinate. Answers "what will conditions look like through 2050?" — the future-projection counterpart to openmeteo_get_historical (ERA5, what happened). Daily resolution only. Available models: CMCC_CM2_VHR4, FGOALS_f3_H, HiRAM_SIT_HR, MRI_AGCM3_2_S, EC_Earth3P_HR, MPI_ESM1_2_XR, NICAM16_8S. A model name outside that list is sent upstream rather than rejected here, so a model Open-Meteo adds later still works; if upstream rejects the request, the error names the offending model on its own rather than the whole requested list. With 2+ models each variable appears once per model with the model name as suffix (e.g. temperature_2m_max_CMCC_CM2_VHR4); a single or omitted model returns plain variable names. Not all models carry all variables — missing combinations return null. Multi-decade daily pulls across several models produce thousands of records and spill to DataCanvas for SQL querying when canvas is enabled, returning a bounded preview with truncated: true when it is not.
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  • GET /search — Cross-resource omni-search Cross-resource search across profiles, rooms, messages (incl. private DMs + group DMs you're in), events, and chapters in one round trip. Returns the top-N matches per resource, grouped by resource. Use this when you don't yet know which resource carries the answer — agents typically call this first, then drill into a specific `GET /search/<resource>` for more depth on a single bucket. There's no page param: when you hit the per-resource limit and want more, switch to the per-resource endpoint for that one. The events slice has a baked-in forward-looking default (events ending in the last 30 days or later, and currently enabled) — this matches the in-app "Search across DC" surface. Use `GET /search/events` directly to look further back in time. **Query syntax (`q=`):** plain words match with prefix + typo tolerance. Wrap a phrase in double quotes to require an exact ordered match — e.g. `q="remote work"`. AND/OR/NOT/parentheses are NOT parsed in `q=` — use the structured filter params below for boolean composition.
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  • List all AI models available on Gonka Network with live pricing. Models work as drop-in replacements for OpenAI and Anthropic — same SDK, same API calls. Use this when user asks which model to use or wants alternatives to GPT-4o / Claude. Returns: model IDs (use directly in openai.chat.completions.create), status, USD per 1M tokens. After this: call calculate_savings() to see annual savings with these models.
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  • 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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  • Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceeds your budget. Returns the optimized plan with substituted models, new per-step costs, total savings, and whether the target_budget is met. Optionally set target_budget to constrain the optimization. Costs 1 credit.
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Matching MCP Servers

  • A
    license
    A
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    maintenance
    Classifies development task complexity (LIGHT/MEDIUM/HEAVY) and recommends the most cost-efficient AI model per provider, enabling optimized model selection for coding tasks.
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    666
    MIT
  • A
    license
    C
    quality
    D
    maintenance
    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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    Apache 2.0

Matching MCP Connectors

  • 55 Concordance-tested financial models with published specs; responses cite assumptions and sources.

  • Turn text or an image into an animation-ready 3D model (GLB): generate, rig, animate, retexture.

  • Get canonical FINN URLs for a brand and its models — for building internal linking blocks on SEO pages. For each model returns three URLs that target DIFFERENT funnels: `mdp_url` (marketing/brand page), `plp_subscribe_url` (subscription product listing, /de-DE/subscribe/{brand}_{model}), and `plp_leasing_url` (leasing product listing, /de-DE/leasing/{brand}_{model}). Use `plp_leasing_url` when linking from a Leasing advisory, `plp_subscribe_url` when linking from subscription content. If `model` is omitted, returns all currently available models for the brand.
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  • Rig a 3D model: generate a skeleton and skin weights for an existing GLB so it can be animated. Accepts a URL or base64-encoded GLB in `model`. Synchronous: the call blocks while the rig is generated, then returns a downloadable `model_url` for the rigged GLB. rig_type selects the skeleton prior — general (default, any asset), humanoid (anime-style characters), game (classic game-character rigs), or the pinned humanoid templates for two-armed, two-legged characters: humanoid_template (standard 22-joint skeleton with named joints, required for animating from the preset library) and humanoid_template_hands (52 joints, five fingers per hand). joint_naming relabels the identified joints to a convention — smpl (default), mixamo, humanik, unreal, godot, rigify, or vroid — without changing the skeleton. Credits are charged only on success. Rigging is non-destructive to geometry but replaces any prior skeleton, so animations made against an old rig no longer apply. Requires an API key (user scope). Credits: This endpoint consumes 1 credits per call.
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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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  • 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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  • Use this when you need to author a NURBS Surface into the user's .kcad.ts. One authoring path, selected by `kind`: - 'nurbs' — insert a nurbsSurface(...) / surfaceFromCurves(...) call. Pass either { controls, degree, weights?, knots?, periodic? } for direct construction, OR { section_sketch_ids } for skinning. Slice-1 limitation: weights are accepted but currently ignored (TColStd_Array2OfReal not exposed in WASM bindings); surfaces are non-rational. - 'boundary' — insert a surfaceFromBoundary([c1,c2,c3,c4], opts?) call: one NURBS face through 4 boundary Curve3D refs (bottom, right, top, left in loop order; adjacent endpoints must coincide within 1e-6 mm) via OCCT BRepOffsetAPI_MakeFilling. The returned Surface produces no Shape until you chain .thicken(t) or .toShape() (do that via add_feature on the binding name). Returns the modified code + diagnostics. Each kind fails closed on its own missing required params.
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  • Get webhook delivery history, either for a resource or for a webhook. Query in exactly one of two modes: - By resource: pass `resource_type` + `resource_id` to see deliveries made for a specific job/monitor/monitor_group. - By webhook: pass `webhook_id` to see every delivery made through one webhook — including manual test deliveries (from `test_webhook`), which are not tied to a job or monitor and only appear in this mode.
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  • Create a new mock REST API project. Returns {id, adminKey, baseUrl, resources[]}. SAVE the adminKey — it is required for admin operations (add_resource, custom_route, snapshots) and is shown only once. Presets seed a full backend: blog (posts/comments/authors), ecommerce (products/orders/customers/reviews), saas (users/teams/events), openai (ready OpenAI-compatible mock — chat completions incl. streaming SSE, embeddings with a real 1536-dim vector, models; point OPENAI_BASE_URL at {baseUrl}/v1). Omit preset for a starter project (one seeded "items" resource — live data immediately, reshape or delete it); use "blank" for a truly empty project you fill via add_resource or import_data. The mock API is then live at baseUrl: standard REST CRUD (GET/POST/PUT/PATCH/DELETE), CORS enabled, no auth needed.
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  • List all AI models available through DPX Compute. All models are free-tier (no token cost) — routed via OpenRouter. Returns model IDs, provider, capability strengths, context window, and speed tier. Use this before compute.route to understand what models are available and pick the right one for a task. Free.
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  • List all vendors tracked by EOSL.ai with family counts and vendor page URLs. Read-only, no parameters. Use this first to check whether a vendor is covered at all; for specific models use search_models, for a part number use lookup_part.
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  • 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.
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  • Convert a single source image into a textured 3D model (image-to-3D). Synchronous: the call blocks while the mesh is generated, decompressed, and re-uploaded, then returns 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 get3DModelResults. Requires an API key (user scope). Credits: This endpoint consumes 3 credits per call.
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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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  • List AI image-generation models exposed to merchants (sanitized — provider/cost details hidden). Use to pick a `modelCode` for `generate_post_cover`.
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  • Get a single EMDB entry by its EMDB id (e.g. "EMD-1080"). Returns a trimmed record: title, sample, structure-determination method, resolution (Angstrom), and release date. EMDB holds 3D electron-microscopy density maps of proteins, complexes, and viruses. Keyless.
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