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510,134 tools. Updated 2026-09-03 21:29

"A search for information related to 3D technology, models, or concepts" matching MCP tools:

  • When to use: Hugging Face Hub models, datasets, Spaces, collections, papers, daily papers, today's trending models, current paper leaderboard, docs, and repository files. Examples: {"operations":[{"cmd":"ls","args":["hf://models/trending","--limit","10"]}]} {"operations":[{"cmd":"ls","args":["hf://papers/trending"]}]} {"operations":[{"cmd":"ls","args":["hf://papers/daily/latest"]}]} Use hf_fs for Hugging Face Hub filesystem operations. Call it with operations, an array of {cmd, args} items; multiple operations may be submitted together. Usage: {"operations":[{"cmd":"ls","args":["hf://models/org/repo"]}]} Grammar; each string below is one args array item: ls URI [--recursive] [--glob GLOB] [--type TYPE] [--sort SORT] [--limit N] cat URI [--offset N] [--max-bytes N] attach URI [--max-bytes N] stat URI find URI [--name GLOB] [--path GLOB] [--type TYPE] [--limit N] search URI [QUERY] [--type TYPE] [--sort SORT] [--tag TAG] [--kind mcp] [--limit N] COMMAND = ls|cat|attach|stat|find|search. TYPE = file|dir|repo|bucket|collection|paper|link. SORT = createdAt|downloads|likes|lastModified|likes30d|trendingScore|mainSize|id|trending|upvotes. URI is a canonical hf:// URI. QUERY and GLOB are each one string. Use search for discovery, ls for a known directory, find for recursive matching within a known scope, stat for filesystem metadata or an uncertain target type, cat for text contents, and attach for a complete JPEG, PNG, or WebP image. When the request gives an exact text-file URI, use cat directly; do not add ls or stat first. stat does not read the contents of JSON, Markdown, or other text files. Search scopes: hf://models|datasets|spaces[/OWNER], hf://collections[/OWNER], hf://papers, and hf://docs[/...]. Paper and documentation search require QUERY. Repeat --tag only for search hf://spaces; --kind mcp selects MCP Spaces. Use ls hf://models/trending, hf://datasets/trending, hf://spaces/trending, or hf://papers/trending for trending listings. For a named paper.md or metadata.json, use cat directly. Use ls on a paper only to discover an unnamed related resource. Omit --limit, --sort, and --type unless the request requires them. Limits and path-specific behavior are documented at hf://README.md. Issue one hf_fs call.
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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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  • Full-text search across all SEC EDGAR filings since 2001 for a keyword or phrase. Wraps EDGAR's own full-text search index, so it covers every filer and form type, not just a single company. Useful for finding who is disclosing a particular risk, technology, litigation, or event across the entire market. When to use: cross-company research ("who is disclosing AI-related risk factors"), finding filings that mention a specific term, litigation or regulatory tracking. When NOT to use: you already know the company (use edgar_filings_feed, which is company-scoped and cheaper), or you need results from before 2001 (EDGAR full-text search does not cover that far back). Args: - query (string, required): search text. Wrap an exact phrase in double quotes, e.g. "\"material weakness\"". - forms (string[], optional): restrict to form types, e.g. ["10-K"]. - dateFrom (string, optional): ISO start date (YYYY-MM-DD). - dateTo (string, optional): ISO end date (YYYY-MM-DD). - limit (integer, optional, default 10): maximum hits to return (1-50). Returns structuredContent: { "query": "material weakness", "totalMatches": 10000, "totalIsApproximate": true, "count": 2, "hits": [ { "id": "0001193125-26-123456:doc.htm", "entity": "Example Corp.", "form": "10-K", "filedAt": "2026-03-01", "cik": "0000320193" } ], "source": "https://www.sec.gov/edgar" } "totalMatches" is a lower bound and "totalIsApproximate" is true once EDGAR's own count exceeds its display cap (10,000) — narrow with forms/dateFrom/dateTo for a precise count.
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  • 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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  • 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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  • Ranked related listings with per-item reasons. Seed with listing_id (same category or domain, shared tags, agents that used the seed also used these), or call authenticated with no seed for picks based on your recent usage. Not a keyword search: use search_catalog for that.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.
    33
    1
    MIT
  • A
    license
    A
    quality
    D
    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.
    3
    41
    MIT

Matching MCP Connectors

  • 61 verified financial models plus household planning and coordination tools; specs cited.

  • Web search for AI agents. Ranked results with page passages already extracted, plus URL to markdown.

  • 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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  • List OECD dataflow refs we have pre-vetted, grouped by topic (gdp, labour, prices, finance, households, health, demographics, projections, tax, education, environment, technology). Pass the flow_ref to fetch_dataset. For everything else use search_dataflows or browse https://data-explorer.oecd.org.
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  • Run one read-only AI-search-readiness audit for a public business domain: company, technology, contact, and DNS/email evidence from `enrich`, plus the live structured-data gap analysis and paste-ready JSON-LD template from `schemaforge`. Use `enrich` for company facts only or `schemaforge` for structured-data remediation only. The template contains placeholders for real data; the score is diagnostic, no site changes are made, and it does not guarantee AI citations.
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  • One sector's drill-down: every member of the sector scored and ranked by opportunity, plus the sector's own ETF row. Accepts a sector name (e.g. 'Energy', 'Information Technology') or its ETF symbol (e.g. XLE, XLK). Unknown values return the sector directory. Free tier: one market day delayed. Live sibling (x402, pay-per-call): get_sector_read_live.
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  • List the exact canonical car makes (brands) TransparentCars can search and price — or, given a make, that brand's models — using the exact strings the other tools expect. Call this FIRST (or whenever unsure of spelling) and pass the returned values verbatim into search_inventory / check_fair_price. No make = the list of brands; with a make = that brand's models.
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  • Get information about related addresses of an input address. Note: This only includes the the "special" connections 'First Funder', 'Signer', 'Previous Signer', 'Multisig Signer of', 'Previous Multisig Signer of', 'Deployed via', 'Deployed by', 'Deployed Contract', 'Created Contract', 'Created by'. To get related wallets, also check address counterparties. First funder exchange withdrawal address does usually NOT belong to the same entity as the address, only deposit addresses. Only information is that it has been funded by the exchange.
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  • AUTHORITATIVE full XBRL fundamentals dump for a US public company by CIK. Returns every reported financial metric (hundreds of concepts: revenue, net income, assets, liabilities, EPS, cash flow lines, segment breakdowns) with annual and historical values pulled straight from the company's SEC filings — the official numbers, not estimates. Use when you need the complete fundamental picture vs. one metric (for one metric use edgar_company_concept). Leads with latest_annual — revenue, net income, assets, cash, EPS for the most recent fiscal year, resolved to whichever XBRL concept the filer currently reports under — and flags retired concepts (e.g. a pre-ASC-606 Revenues tag) as stale so a 2010 figure is never mistaken for current. Large payload; agents typically use this once to discover available concepts then narrow to edgar_company_concept for follow-up queries.
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  • Returns one or more Agrus case studies (NDA-protected; customer names are kept private, codenames + technology + outcomes are open). Filter by slug or vertical, or call with no args to list all. Use this for proof of prior work.
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  • Search V³ News for tracked geopolitical events. Use when the user asks what is happening on a topic, in a country, or in a domain (e.g. security, energy, finance, technology). Returns a ranked list of events with why-it-matters, risk/impact/signal scores, and a v3.news link for each. Args: query: free-text topic (e.g. "Iran sanctions", "Taiwan"). Optional. country: ISO-3166 alpha-2 code to filter by (e.g. "US", "CN"). Optional. category: V³ category slug — one of geopolitics, security_risk, energy_resources, finance, markets, macroeconomics, public_finance, trade_supply, technology, science_biosecurity, environment_climate, business. Optional. limit: max results, 1-20 (default 10).
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  • 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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  • Return a list of hands-on SecDim Play secure coding challenges (labs) related to a detected or suspected vulnerability. SecDim Play challenges are scored, hands-on labs: find and fix a real vulnerability in running code to earn points and badges. Use this tool to: - Find hands-on SecDim Play labs for specific vulnerabilities like XSS, SQL Injection, etc. - Explore OWASP Top 10 vulnerabilities and related labs - Provide additional resources and guides to help developers improve their secure coding skills For structured tutorial content (text, video, and lab-based courses) on the same vulnerability, use search_learn_courses (SecDim Learn) instead or in addition. Args: search: Search term for the vulnerability (e.g., 'xss', 'sql-injection', 'injection') cwe: Common Weakness Enumeration (CWE) ID to filter by owasp: OWASP category to filter by (e.g., 'a03:2021') technology: Technology or framework to filter by (e.g., 'react', 'django') language: Programming language to filter by (e.g., 'javascript', 'python') difficulty: Difficulty level to filter by (e.g., 'trivial', 'easy', 'medium', 'hard') type: Challenge format to filter by (e.g., 'battle', 'exploitation', 'incident-response') mitre: MITRE ATT&CK ID to filter by (e.g., 'T1102.003') SecDim Play challenges (labs) each simulate a real vulnerability. They are scored according to the following difficulty levels: - Trivial: Easy to find and path vulnerabilities. It can be completed in 5-10 minutes. 1-15 points. - Easy: Known vulnerabilities. It can be completed in 10-30 minutes. 16-35 points. - Medium: Known vulnerabilities but require defence-in-depth patch. It can be completed in 20-30 minutes. 36-70 points. - Hard: Hard to find or patch vulnerabilities. It can be completed in 30-60 minutes. 71-100 points. - Battle: SecDim Flagship attack and defence challenge that require both vulnerability exploitation and mitigation skills. Points are accumulated. Returns: Dictionary containing SecDim Play labs results or error If there are no results, user can perform a manual search on the SecDim Play frontend (SECDIM_PLAY_FRONTEND_BASE_URL)
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  • Search SecDim Learn courses. SecDim Learn provides tutorial-based courses (mixing video, text and hands-on lab topics) covering secure coding, secure design, vibe coding security, devsecops, and cloud security. Many courses are complementary or prerequisite to hands-on, scored SecDim Play challenges/labs. Use this tool to: - Browse the SecDim Learn course catalogue - Find courses related to a topic, language, or technology (e.g. "OWASP Top 10", "fuzzing", "Python") Args: search: Optional search term to filter courses by title, description, or tags. If omitted, returns the full course catalogue. Returns: Dictionary with a "courses" list. Each course includes its title, description, image, slug, tags, numeric "level" (1=beginner, 2=intermediate, 3=advanced) and a "difficulty" label. Use get_learn_course with a course's slug to view its syllabus of topics.
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  • Count the exact number of tokens in a text string for a specific AI model. Uses tiktoken for OpenAI models and estimates for others. Args: text: The text to count tokens for model: The AI model to count tokens for. Options: gpt-4o, gpt-4o-mini, gpt-4.1, claude-sonnet, claude-haiku, gemini-pro, gemini-flash, llama-4, deepseek-v3, mistral-large. Default: gpt-4o Returns: Token count information including count, context window, and fit status
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