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167,701 tools. Last updated 2026-06-03 01:25
"How to Create a 3D Model Using Blender" matching MCP tools:
- Convert a single photo into a textured 3D GLB model. Uses Seed3D — generates accurate geometry and materials from one image. 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'.Connector
- Returns available payment and authentication options for accessing live market data. Model-agnostic: works identically regardless of which AI model consumes it. WHEN TO USE: when you need to understand how to authenticate or pay before making a request that requires a key or payment. Returns upgrade ladder: sandbox (200 calls free), x402 per-request ($0.001 USDC), x402 sandbox (10 credits for $0.001), credit packs ($5 = 1000 calls), builder subscription ($99/mo = 50K/day). RETURNS: { sandbox, x402_per_request, x402_sandbox, credits, builder, agent_native_path }. No authentication required. Always returns 200.Connector
- Returns the canonical guide for using TMV from a coding-agent context. Covers the fix-test-retest loop, how to write a good test prompt, how to read the actionTrail / consoleErrors / failedRequests outputs, and common gotchas. Call this first if you're a new agent on a project — it'll save you a debug session. The same content is served at https://testmyvibes.com/docs/coding-agents.Connector
- Returns available payment and authentication options for accessing live market data. Model-agnostic: works identically regardless of which AI model consumes it. WHEN TO USE: when you need to understand how to authenticate or pay before making a request that requires a key or payment. Returns upgrade ladder: sandbox (200 calls free), x402 per-request ($0.001 USDC), x402 sandbox (10 credits for $0.001), credit packs ($5 = 1000 calls), builder subscription ($99/mo = 50K/day). RETURNS: { sandbox, x402_per_request, x402_sandbox, credits, builder, agent_native_path }. No authentication required. Always returns 200.Connector
- Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.Connector
Matching MCP Servers
- AlicenseBqualityBmaintenanceTurn natural language into 3D models in Fusion 360. 64 CAD tools including sketches, extrudes, fillets,and JIS standard parts.Last updated652MIT
- Alicense-qualityCmaintenanceA CLI tool that sets up a Model Control Protocol server and deploys it to Cloudflare Workers, allowing you to quickly create custom tools for your Cursor Agent just by writing TypeScript functions.Last updated7816MIT
Matching MCP Connectors
Create, edit, preview, publish, and manage web pages from MCP-capable AI clients.
Turn text or an image into an interactive, textured 3D model (GLB). By three.ws.
- WHEN: you need ALL objects of a given type or in a given model. Triggers: 'list all tables in ALM', 'show all classes', 'quels objets dans le modèle', 'give me all forms'. Full index scan -- returns EVERY matching object, not just top search results. Use to discover what tables, classes, forms, enums, etc. exist in a specific model. When no filters are given and a custom model is configured, defaults to listing that model. NOT for a single object -- use get_object_details. NOT for natural language search -- use search_d365_code.Connector
- Retrieve static game rules, denomination model, pot mechanics, and strategy explanations. Free -- no payment required. Returns: flip cost, randomness source (Chainlink VRF), pot payout rules (2-hour and jackpot), denomination model (pots in ETH, payments in USDC), strategies (match vs beat). Call this first to understand the game before using other tools. [pricing: {"cost":"0","currency":"USDC","type":"free"}]Connector
- Get Helium's proprietary ML model-predicted price for a specific option contract. Helium trains per-symbol regression models on historical options data. This tool looks up the most recent available options chain for the symbol (today or up to 5 days back), finds the exact contract matching strike/expiration/type, and runs it through that model to produce a predicted fair-value price. Returns: - symbol: the ticker - strike: the strike price used - expiration: the expiration date used - option_type: 'call' or 'put' - predicted_price: Helium's model-predicted option price in dollars - prob_itm: probability of expiring in the money (0.0–1.0), or null if model unavailable - options_data_date: the date of the options chain snapshot the model was run on (so you know how fresh the underlying market data is) Throws an error if no options chain data is available for the symbol within the past 5 days, or if the exact contract (strike/expiration/type combination) does not exist in that chain. Args: symbol: Ticker symbol, e.g. 'AAPL', 'SPY'. strike: Strike price as a number, e.g. 150.0. expiration: Expiration date as 'YYYY-MM-DD', e.g. '2026-06-20'. option_type: Must be 'call' or 'put'.Connector
- WHEN: developer wants to see what custom/extension objects exist in their model. Triggers: 'list my custom objects', 'what have we customized', 'show ISV objects', 'list custom model', 'what objects are in our model'. List all D365 F&O objects in the custom/extension model directory on disk. Reads the file system directly -- always reflects the latest uncommitted state. Pass `customModelPath` to specify a model directory; or set it once via the `D365-Custom-Model-Path` header in your .mcp.json (applies to all tool calls automatically).Connector
- Get a compound's default 3D conformer — atomic coordinates and bonds — for one CID. format="json" (default) returns parsed atoms and bonds the model can reason over directly; format="sdf" returns the raw V2000 SDF text for passthrough to docking, rendering, or conformer tools. Optionally lists alternate conformer IDs. Not every compound has computed 3D coordinates (large molecules, mixtures, and some salts do not).Connector
- Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.Connector
- Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.Connector
- Creates a visual edit session so the user can upload and manage images on their published page using a browser-based editor. Returns an edit URL to share with the user. When creating pages with images, use data-wpe-slot placeholder images instead of base64 — then create an edit session so the user can upload real images.Connector
- List all attributes (properties) of a specific Smart Data Model, including each attribute's NGSI type (Property, GeoProperty, or Relationship), data type, description, recommended units, and reference model URL. Use this after get_data_model when the user wants to understand what fields a model has, what values they accept, or how to construct a valid NGSI-LD payload. Example: get_attributes_for_model({"model_name": "WeatherObserved"})Connector
- Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.Connector
- Create a new funnel on a project. Steps are 2–10 ordered events or pageview paths. conversionWindowMs caps how long a visitor has between consecutive steps (default 7 days); this is the step-to-step limit, without which a funnel is just event co-occurrence. Returns { id } on success.Connector
- Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) Queries executed using the `execute_sql_readonly` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `project_id` field.Connector
- Search for a data model by approximate or misspelled name using fuzzy matching. Use this as the recovery step whenever get_data_model returns MODEL_NOT_FOUND — it finds the closest real model names even when the spelling is off. Returns ranked candidates with similarity scores. Example: fuzzy_find_model({"model_name": "WeatherFora", "threshold": 80})Connector
- Explain the Guard product using CurrencyGuard's approved product and FAQ content. Covers: what the Guard is, how it works, who it is for, how it compares to forwards or options, and legal, regulatory, accounting, or eligibility questions.Connector