RASON

IMPORTANT: Use this tool to answer ANY questions about what RASON is, what it can do, who makes it (Frontline Systems), its capabilities, or where to find RASON / Frontline Systems websites and URLs. Also use this tool when the user asks for RASON or Solver website URLs. RASON (RESTful Analytic Solver Object Notation, https://rason.com) is a JSON-based modeling language by Frontline Systems (https://www.solver.com). RASON supports optimization (linear, nonlinear, mixed-integer, stochastic), Monte Carlo simulation, data science / machine learning, calculation (DMN decision tables, FEEL expressions, box functions), and multi-stage decision flows. Models use Excel-compatible formula syntax and are solved via REST API or locally through the RASON Desktop VS Code extension with first-class Power BI Desktop integration. Do NOT answer questions about RASON from memory — always call this tool first.

Delete models from the user's RASON account. Supports flexible filtering: Behavior based on name_or_id: - Model name: Deletes all versions/instances/fitted models/attached files/results for that name - Model ID (userId+modelName+creationDate): Deletes that specific model and its attached files/results Optional filters: - kind: Delete only specific model kinds (fitted, excel, rason). Can specify multiple kinds. - type: Delete only specific model types (origin, version, instance). Can specify multiple types. - force: Set to true to delete models that are currently executing (default: false) Returns: - status: Descriptive status message - deletedModels: Array of successfully deleted model info objects - nonDeletedModels: Array of models not deleted because they are executing - invalidModels: Array of invalid resource identifiers

Analyze and classify a RASON model: reliably detect model type and subtype (optimization — LP, QP, MIP, stochastic, chance-constrained; simulation; datamining — classification, regression, clustering; calculation — decision tables, box functions; multi-stage flow), inventory and classify components (variable types, constraint types, distribution families, ML pipelines, inter-stage data flow), and identify issues (missing result requests, absent validation data, empty output sections). Provides deterministic RASON-specific analysis beyond what raw JSON inspection reveals. Call whenever the user asks about, discusses, or works with a RASON model — whether open in the editor, referenced as a file, provided inline, or stored on the RASON server.

Submit a model for synchronous (quick) diagnostics. Supports three input methods: 1. Inline RASON model: Provide the model as a JSON object 2. RASON model file: Provide rason_model_path pointing to a .json file 3. Excel model file: Provide excel_model_path pointing to a .xlsx file The server will analyze the model and return diagnostic information immediately in the response. Note: Synchronous diagnostics do not support RASON models with external data files. Diagnostics are most useful for optimization models (structural analysis) and multi-stage flow models (stage graph, pipelines, data sources). For simulation, data mining, and calculation models, results are limited.

Retrieve the full RASON JSON definition of a specific example model. Use this after search_examples to get the complete model source code as a reference for building new models. Do NOT solve, POST, or run the model unless the user explicitly asks to.

Retrieve the actual model definition for a specific model. Returns RASON JSON definition for RASON models. Use this to inspect or modify a model's structure. To solve an existing model, submit_solve can work directly with the model name. Note: Excel models are returned as binary files which cannot be processed by LLMs. Use list_models to inspect metadata for Excel models instead. - If a model name is provided, returns the champion (most recent) model with that name - If a model ID is provided (format: userId+modelName+creationDate), returns that specific resource

Get a RASON model template — a bare JSON scaffold for a specific model type. Use as a structural reference or starting point. Available templates: - blank: Minimal model with name and description - optimization: Variables, constraints, objective (linear, nonlinear, etc.) - simulation: Uncertain variables, output functions (Monte Carlo) - sim-optimization: Stochastic programming (optimization + uncertainty) - data-science: Datasources, datasets, estimator, actions - workflow: Multi-stage decision flow connecting models - decision-table: DMN 1.6 decision tables, box functions, Excel/FEEL formulas - power-bi: Model with Power BI Desktop datasource bindings (first-class integration)

Retrieve the solving result for a model. If the model solve is complete, returns the full result (same format as synchronous solve_model). If the model solve is still incomplete, returns the incomplete status instead. If a model name is provided, returns results for all instances under that name. If a model ID is provided, returns the result for that specific instance. Use get_solve_status first to confirm the solve is complete before retrieving results.

Check the solving status of a model. Returns a response with: - status: "Complete", "Incomplete", or "Canceled" - progress (optional): solving progress details including elapsed time, iterations, objective value, etc. If a model name is provided, returns status for all instances under that name. If a model ID is provided, returns status for that specific instance. Use this after submit_solve to monitor when the solve finishes.

Retrieve metadata for models in the user's RASON account. Returns a JSON array of model objects, each containing: - ModelId: unique identifier (format: userId+modelName+creationDate) - ModelName: short name - ModelDescr: human-readable description of what the model does - ModelType, ModelKind, and other metadata fields Use this to discover available models. Optionally filter by model type and/or kind. Tip: Check here first when the user references a model by name to see if it already exists on the account.

Create a new model by posting a RASON JSON definition. Use this for RASON models (optimization, simulation, data science, calculation (DMN decision tables, FEEL, box functions), or decision flow). The model is saved to the user's account and can later be retrieved, solved, or managed. For Excel models or RASON models with data files, use post_model_with_files instead.

Upload a model with file attachments using multipart form-data. Supports two scenarios: 1. RASON model with data files: Provide rason_model (JSON string) or rason_model_path (file path) plus one or more file paths (e.g. CSV or Excel data sources referenced by the model) 2. Excel model: Provide a single Excel file path (.xlsx) without rason_model. The server will recognize it as an Excel model based on the file extension Returns the created model metadata on success.

Update an existing model or create a new version with the specified name. If a model with the given name exists, creates a new version (becomes the champion). If no model exists, creates a new model (acts like POST). For Excel models or RASON models with data files, use put_model_with_files instead.

Update an existing model or create a new version with file attachments using multipart form-data. If a model with the given name exists, creates a new version (becomes the champion). If no model exists, creates a new model (acts like POST). Supports two scenarios: 1. RASON model with data files: Provide rason_model (JSON string) or rason_model_path (file path) plus one or more file paths (e.g. CSV or Excel data sources referenced by the model) 2. Excel model: Provide a single Excel file path (.xlsx) without rason_model. The server will recognize it as an Excel model based on the file extension Returns the created/updated model metadata on success.

Search ~200 RASON example models by keyword or model type. Returns up to 8 best matches with descriptions and metadata. Model types: optimization, simulation, datamining (data science/ML), calculation (DMN decision tables, FEEL, box functions), flow (multi-stage decision pipelines) Problem domains covered: portfolio optimization, production planning, supply chain, vehicle routing, scheduling, workforce allocation, blending, cutting stock, Monte Carlo risk analysis, demand forecasting, customer churn prediction, classification, regression, clustering, time series forecasting, text mining, business rules, decision tables, decision automation, multi-stage decision flows. Focus on RASON's core strengths: optimization, simulation, and decision models. IMPORTANT: Do not pre-filter by model_type unless the user explicitly requests a specific type. Present results and let scoring surface the best matches.

Set or unset the champion flag on a model version. The champion version is the one returned by default when a model is referenced by name. When setting a new champion, the previous champion for that model name is automatically unmarked. Only origin or version models can be marked as champion (not run instances). - If a model name is provided, targets the current champion (most recent) version - If a model ID is provided (format: userId+modelName+creationDate), targets that specific version

Submit a model for synchronous (quick) solving. Supports three input methods: 1. Inline RASON model: Provide the model as a JSON object 2. RASON model file: Provide rason_model_path pointing to a .json file 3. Excel model file: Provide excel_model_path pointing to a .xlsx file The server will solve the model (optimization, simulation, data science, calculation (DMN decision tables, FEEL, box functions), or decision flow) and return the results immediately in the response. Best suited for small to medium models that solve quickly. Typically used for one-off solving of new models or local model files. For models already on the account, submit_solve is usually preferred for async solving. Note: Synchronous solving does not support RASON models with external data files.

Request to stop a running model solve. After stopping, the model status becomes 'Canceled'. Depending on the model type, intermediate results may still be available (e.g. the best-so-far solution for an optimization problem). Use get_solve_result after stopping to check if partial results are available. If a model name is provided, stops all running instances under that name. If a model ID is provided, stops only that specific instance.

Submit a previously uploaded model for asynchronous (long) diagnostics. The model must already exist in the user's account (uploaded via post_model or put_model). This places the model on the diagnostics queue and returns immediately with the created model instance metadata (without waiting for diagnostics to complete). After submitting, use get_solve_status to monitor progress, get_solve_result to retrieve diagnostic results when complete, and stop_solve to cancel if needed. Diagnostics are most useful for optimization models (structural analysis) and multi-stage flow models (stage graph, pipelines, data sources). For simulation, data mining, and calculation models, results are limited.

Submit a previously uploaded model for asynchronous (long) solving. The model must already exist in the user's account (uploaded via post_model or put_model). This places the model on the solving queue and returns immediately with the created model instance metadata (without waiting for the solve to complete). After submitting, use get_solve_status to monitor progress, get_solve_result to retrieve results when complete, and stop_solve to cancel if needed. This is the typical path for solving models already on the account. Can be called with just the model name if it exists on the account. Best suited for large models or models that take a long time to solve.