# Technical Remediation Report: Resolving Circular Serialization Failures in Mendix Model SDK Integration via Model Context Protocol within Google Antigravity
## Executive Summary
This comprehensive technical report addresses a critical systems integration failure encountered during the development of a Model Context Protocol (MCP) server designed to bridge the Mendix Model SDK with the Google Antigravity agentic development environment. The failure, characterized by a `TypeError: Converting circular structure to JSON` as detailed in the user's "Error Review Report," represents a fundamental architectural collision between the stateful, cyclic graph structure of the Mendix Metamodel and the strict, acyclic serialization requirements of the JSON-RPC transport layer utilized by the MCP standard.
The investigation synthesizes data from the Mendix Platform SDK documentation, the Model Context Protocol specification, and the operational constraints of the Google Antigravity IDE. The analysis reveals that the Mendix Model SDK utilizes a sophisticated, MobX-backed state tree to represent application models, where elements maintain active references to their containers and associations. When an AI agent within Antigravity attempts to "read" these models directly via an MCP tool, the default serialization mechanism (`JSON.stringify`) enters an infinite recursive loop, crashing the server process.
Furthermore, the deployment environment—Google Antigravity—imposes specific configuration requirements regarding environment variable injection and process isolation that complicate the remediation. The report outlines a multi-faceted solution strategy involving the implementation of a Data Transfer Object (DTO) transformation layer to sanitize model data, the configuration of secure `mcp_config.json` parameters for authentication, and the adoption of granular "lazy loading" patterns to optimize performance within the agent's context window.
## 1. Introduction: The Convergence of Low-Code and Agentic Architectures
The integration of Low-Code Application Platforms (LCAP) like Mendix with Agentic AI environments like Google Antigravity represents a paradigmatic shift in software engineering. This convergence aims to move beyond simple code autocompletion to a higher level of abstraction where autonomous agents can reason about, modify, and optimize entire application architectures.
### 1.1 The Operational Context
The user is constructing an MCP server—a standardized interface that allows Large Language Models (LLMs) to interact with external tools and data—to expose the contents of a Mendix application model to Google Antigravity. The Mendix Model SDK is the primary instrument for this interaction, providing programmatic access to the Mendix Metamodel (Domain Models, Microflows, Pages) via TypeScript/JavaScript.¹ Google Antigravity serves as the host environment, an "agent-first" IDE that orchestrates these interactions to assist the developer.³
### 1.2 The Incident Description
During the execution of a tool designed to read the Mendix model, the system encountered a catastrophic error. The user requested Antigravity to generate an error report, which identified the root cause as a `TypeError: Converting circular structure to JSON`.⁵ This error is symptomatic of a deeper mismatch between the data structures provided by Mendix and the data structures expected by the MCP client.
### 1.3 Scope of Analysis
This report provides an exhaustive analysis of the failure vector, dissecting the internal memory structures of the Mendix SDK and the transport mechanics of the MCP. It evaluates the specific constraints of the Antigravity environment, particularly regarding configuration and security. Finally, it proposes a detailed, code-level remediation strategy to decouple the cyclic Mendix model from the serialization process, ensuring robust and secure agentic interaction.
## 2. Architectural Analysis of the Failure Mechanism
To understand the specific error reported (`Converting circular structure to JSON`), it is necessary to examine how data is represented in memory by the Mendix Model SDK and how the JavaScript runtime attempts to process that data for network transmission.
### 2.1 The JavaScript Object Serialization Protocol
The Model Context Protocol relies on JSON-RPC 2.0 for communication between the client (Antigravity) and the server (the user's MCP script).⁶ This protocol necessitates that all data transmitted in the result field of a response be valid JSON. In the Node.js runtime, this serialization is typically handled by the native `JSON.stringify()` method.
The `JSON.stringify()` algorithm operates by traversing the object graph. For each property in an object, it attempts to convert the value to a JSON-compatible string. If it encounters an object, it recursively enters that object to serialize its properties.
* **Acyclic Graphs:** In a standard tree structure (e.g., a simple directory listing), parents reference children, but children do not reference parents. The serializer reaches the leaf nodes and terminates.
* **Cyclic Graphs:** If an object contains a reference to itself, or if a chain of references leads back to a visited object (e.g., A -> B -> A), the serializer enters an infinite loop. The V8 JavaScript engine detects this condition to prevent a stack overflow and throws the `TypeError` observed in the error report.⁵
### 2.2 The Mendix Metamodel: A Graph, Not a Tree
The Mendix Model SDK is not a simple data fetcher; it is a sophisticated Object-Relational Mapping (ORM) system for the Mendix Metamodel. The Metamodel is defined as a graph where elements are highly interconnected to support navigation and integrity checking.¹
#### 2.2.1 Bi-Directional Relationships
In Mendix, relationships between model elements are almost always bi-directional in memory, even if they appear uni-directional in the visual modeler.
* **Containment:** Every element in the model (e.g., an Entity) has a `container` property that points to the element that owns it (e.g., the DomainModel). The DomainModel in turn has an `entities` property containing the Entity. This creates an immediate cycle: `DomainModel.entities.container === DomainModel`.⁹
* **Associations:** An Association element links a Source entity and a Target entity. The Association object references both entities, and the entities may reference the association (or the module containing it).
* **Generalization:** An entity that inherits from another entity maintains a reference to its super-class (Generalization), which acts as a pointer to another Entity object, creating complex cross-module cycles.²
#### 2.2.2 Internal State Management
Beyond the logical model structure, the Mendix SDK objects (instances of `mendixmodelsdk` classes) contain internal state management properties used by the framework.
* **MobX Observables:** The SDK uses MobX to track changes to the model so it can generate deltas for the Model Server. These observable wrappers often contain internal links to listeners and dependency graphs, which are themselves circular.¹¹
* **Transport Handles:** Objects may hold references to the ModelUnit or AbstractModel, which hold references to the WebSocket client or HTTP transport used to communicate with the Mendix Team Server.¹⁰
### 2.3 The Collision Point
The error occurs at the precise moment the MCP server attempts to hand off the data to the client.
1. **Action:** The Agent calls a tool, e.g., `get_domain_model(module: "MyModule")`.
2. **Execution:** The MCP server script calls `await domainModel.load()` to fetch the data from Mendix.¹²
3. **Result:** The script obtains a fully loaded DomainModel JavaScript object.
4. **Mistake:** The script returns this object directly: `return { content: ... }`.
5. **Failure:** `JSON.stringify` encounters the `container` property (or internal MobX properties) and throws the `TypeError`, causing the MCP server process to crash or return an error frame to Antigravity.⁵
## 3. The Mendix Metamodel Ecosystem
A deep understanding of the Mendix Metamodel is essential for constructing the remediation strategy. The MCP server must navigate this ecosystem to extract meaningful data without triggering serialization errors.
### 3.1 SDK Components and Hierarchy
The Mendix SDK is divided into distinct layers, each playing a role in how data is accessed and represented.
| Component | Description | Relevance to Error |
| :--- | :--- | :--- |
| **Platform SDK** | Handles project-level operations: creating apps, managing branches, committing changes, and retrieving the OnlineWorkingCopy.¹ | Low. These objects are usually not serialized deeply. |
| **Model SDK** | Handles the internal logic of the app: Domain Models, Microflows, Pages. Defines the classes for every element type.² | **High.** These are the objects causing the circular reference error. |
| **Model Server** | The backend service that stores the model delta. The SDK acts as a client to this server. | Indirect. The SDK maintains open sockets to this server, which cannot be serialized. |
| **Metamodel** | The abstract definition of the Mendix language (JSON schema). | The source of truth for the property names and relationships. |
### 3.2 The Structure of Model Units
Data in Mendix is organized into "Units." A Unit is the smallest chunk of the model that can be loaded independently. Examples include a Module, a DomainModel, or a Page.
#### 3.2.1 The Loading Pattern
Mendix uses a lazy-loading pattern to manage memory. When you first open a WorkingCopy, you only have the "Interfaces" of the units—lightweight objects containing only the ID and name. To access the contents (e.g., the entities inside a domain model), you must explicitly call `.load()`.¹¹
* **Unloaded Unit:** Safe to serialize (mostly), but contains no useful information (empty entities array).
* **Loaded Unit:** Contains all the data, but is structurally circular.
This creates a dilemma for the developer: if they don't load the unit, the AI agent sees nothing. If they do load it, the serialization crashes.
### 3.3 Domain Model Entities: The Primary Data Source
For most AI agents, the Domain Model is the most critical part of the application to understand, as it defines the data schema.
* **Entities:** Represents database tables. Properties include name, documentation, generalization.⁹
* **Attributes:** Represents columns. Properties include name, type (String, Integer, etc.), defaultValue.
* **Associations:** Represents foreign keys. Properties include owner, parent, type (Reference/ReferenceSet).
The snippet⁹ lists the properties of the Entity class. Note the mix of data (name, documentation) and structural references (container, model, unit). The remediation strategy must explicitly separate these.
## 4. The Model Context Protocol (MCP) Architecture
The Model Context Protocol establishes the rules of engagement between the AI (Antigravity) and the Tool (Mendix SDK). Understanding its constraints explains why the error propagates the way it does.
### 4.1 JSON-RPC and Transport Layers
MCP uses JSON-RPC 2.0 messages exchanged over a transport.⁶
* **Stdio Transport:** The MCP server runs as a subprocess of the IDE. Communication happens via standard input (stdin) and standard output (stdout). This is the default for local integrations in Antigravity.¹³
* **SSE Transport:** The MCP server runs as a standalone web server, and communication happens via Server-Sent Events (SSE) and HTTP POST.¹⁴
In both cases, the payload is text. There is no shared memory. The server *must* serialize its response. The MCP SDK for TypeScript (`@modelcontextprotocol/sdk`) provides helper functions to manage this connection, but it assumes the user provides serializable data.¹⁵
### 4.2 The Role of Zod Schemas
The MCP SDK uses Zod, a TypeScript schema validation library, to define the input arguments for tools.¹⁶ However, it does *not* enforce strict schemas on the *output* of tools. The output is defined simply as a `CallToolResult`, which contains a list of `Content` objects (Text or Image).
* **Implication:** Because the output schema is loose (`z.any()` effectively), the TypeScript compiler does not warn the developer that they are returning a circular Mendix object. The error is only caught at runtime during the serialization phase.
### 4.3 Security and Resource Access
The MCP specification includes provisions for authorization and resource access.
* **Tools:** Executable functions (e.g., "Create Entity"). These are model-controlled.
* **Resources:** Read-only data (e.g., "The Domain Model"). These are application-controlled.⁷
* **Prompts:** Pre-defined templates.
The error in question occurred during a "Read" operation. Whether implemented as a Tool (`get_model`) or a Resource (`mendix://model`), the serialization requirement remains identical.
## 5. The Google Antigravity Environment
Google Antigravity is the specific execution context for this integration. Its unique features and constraints play a significant role in how the solution must be configured.
### 5.1 Agent-First IDE Paradigm
Antigravity differs from traditional IDEs by elevating the AI Agent to a "Manager" or "Architect" role.⁴
* **Mission Control:** A dedicated interface for managing asynchronous agents.
* **The Editor Agent:** A sub-agent that interacts with the code editor.
* **The Terminal Agent:** A sub-agent that can execute commands.
The "Error Review Report" mentioned by the user was likely generated by one of these agents after observing a failure in a background task. The opacity of this reporting—often summarizing "Something went wrong" rather than showing the stack trace—makes manual log inspection critical.¹⁹
### 5.2 The mcp_config.json Configuration
To register an MCP server in Antigravity, the developer must modify the `mcp_config.json` file located in the user's home directory (e.g., `~/.gemini/antigravity/mcp_config.json`) or the project root.²⁰
This configuration file controls:
1. **Command:** The executable to run (e.g., node, npx).
2. **Args:** Arguments for the command (e.g., path to script).
3. **Env:** Environment variables injected into the server process.
**Critical Constraint:** Antigravity agents do not automatically inherit the shell environment of the user. If the Mendix Personal Access Token (PAT) is set in the user's `.bashrc` or `.zshrc`, the MCP server will likely fail to authenticate unless explicitly passed in the `env` block of `mcp_config.json`.²¹
### 5.3 Security Sandboxing
Antigravity employs strict sandboxing to prevent agents from executing malicious code.²²
* **Network Access:** The Mendix SDK requires outbound access to the Mendix Public Cloud APIs (`model.mendix.com`, `sprintr.home.mendix.com`). If Antigravity is running in a "Secure Mode" or behind a corporate proxy without proper configuration, these calls will hang or fail.
* **Filesystem Access:** The SDK creates `.workingcopy` files to cache the model. The MCP server process must have write permissions to its working directory.
## 6. Diagnostic Synthesis
Combining the analysis of the Mendix Metamodel, the MCP transport layer, and the Antigravity environment, we can definitively construct the sequence of the failure and the necessary conditions for a fix.
### 6.1 The Failure Chain
1. **Trigger:** User asks Antigravity to "Read the domain model."
2. **Agent Action:** Antigravity invokes the `read_domain_model` tool on the MCP server via JSON-RPC.
3. **SDK Execution:** The MCP server uses the Mendix Platform SDK to authenticate (using PAT) and download the model metadata.
4. **Loading:** The script calls `domainModel.load()`, populating the memory with interconnected, cyclic Entity and Association objects.
5. **Serialization Attempt:** The script attempts to return this object graph to Antigravity.
6. **Crash:** `JSON.stringify` detects the cycle (Entity -> DomainModel -> Entity) and throws `TypeError`.
7. **System Reaction:** The MCP server process terminates or emits a panic log. Antigravity detects the pipe closure and reports a generic error.
### 6.2 The "Wall"
The "wall" described by the user is the inability to proceed because:
1. They cannot see the error (hidden in logs).
2. They cannot fix the error by "trying harder" (the data structure is fundamentally incompatible).
3. They cannot use the default `serializeToJs` method because it produces code, not data.
## 7. Remediation Strategy I: The Data Transfer Object (DTO) Pattern
The most robust solution is to implement a transformation layer that maps the complex Mendix SDK objects to simple, acyclic Data Transfer Objects (DTOs). This "Sanitization" ensures that the MCP server returns clean JSON that the AI can easily parse.
### 7.1 Concept: Whitelisting vs. Blacklisting
Attempting to "Blacklist" properties (e.g., "remove container") is risky because Mendix objects have many internal properties that might cause issues. The superior approach is "Whitelisting": explicitly defining a new interface (DTO) and copying only the desired primitive values.
### 7.2 Defining the DTO Schemas
We must define TypeScript interfaces for the data we want to expose.
```typescript
// DTO Definitions
interface DomainModelDTO {
id: string;
moduleName: string;
entities: EntityDTO;
associations: AssociationDTO;
}
interface EntityDTO {
name: string;
documentation: string;
location: { x: number; y: number };
attributes: AttributeDTO;
generalization?: string; // Name of parent entity, not the object
}
interface AttributeDTO {
name: string;
type: string; // e.g., "String", "Integer"
defaultValue?: string;
}
interface AssociationDTO {
name: string;
source: string; // Name of source entity
target: string; // Name of target entity
type: string; // "Reference" or "ReferenceSet"
}
```
### 7.3 The Transformation Logic
The remediation requires writing a helper function `serializeDomainModel` that takes a raw `domainmodels.DomainModel` and returns a `DomainModelDTO`.
* **Handling Entities:** Map over the entities array. For each entity, extract the name and attributes.
* **Handling Generalization:** Instead of returning the Generalization object (which links to the super-entity), just return the *qualified name* of the super-entity (e.g., "System.User").
* **Handling Associations:** Extract the names of the source and target entities.
**Crucial Detail:** Because Associations are separate elements in the Domain Model (not nested inside entities in the metamodel), they must be processed separately and linked logically by the AI.
## 8. Remediation Strategy II: Cycle Detection and Graph Traversal
If the requirement is to dump the *entire* model without manually defining DTOs for every possible element type (Pages, Microflows, JSON Structures, etc.), a generic graph traversal with cycle detection is required.
### 8.1 The safeSerialize Function
This function utilizes a `WeakSet` to track visited objects during serialization.
```typescript
function safeSerialize(obj: any, visited = new WeakSet()): any {
// 1. Primitive Check
if (obj === null || typeof obj !== 'object') {
return obj;
}
// 2. Cycle Detection
if (visited.has(obj)) {
return "[Circular]";
}
visited.add(obj);
// 3. Array Handling
if (Array.isArray(obj)) {
return obj.map(item => safeSerialize(item, visited));
}
// 4. Object Handling
const cleanObj: any = {};
for (const key in obj) {
// Filter out Mendix internal properties and private fields
if (key.startsWith("__") || key === "container" || key === "model" || key === "unit") {
continue;
}
// Recursively serialize
if (Object.prototype.hasOwnProperty.call(obj, key)) {
cleanObj[key] = safeSerialize(obj[key], visited);
}
}
return cleanObj;
}
```
### 8.2 Comparison of Strategies
| Strategy | Pros | Cons | Recommendation |
| :--- | :--- | :--- | :--- |
| **DTO (Explicit)** | **Cleanest output.** Optimized for LLM token usage. No internal noise. | Requires maintenance. Must write mappers for every element type. | **High.** Best for specific tools (e.g., "Get Entities"). |
| **Safe Traversal** | **Universal.** Works on any Mendix object without extra code. | **Noisy.** Output contains internal IDs and verbose structures. Wastes tokens. | **Medium.** Use for exploration or debugging tools. |
| **serializeToJs** | **Native.** Built-in to SDK. No crashes. | **Wrong Format.** Returns imperative code, not a data schema. Hard for LLM to query. | **Low.** Use only if the AI's goal is to write scripts. |
## 9. Implementation Roadmap
This section provides the step-by-step instructions to implement the DTO strategy within the Antigravity/MCP context.
### 9.1 Step 1: Secure Environment Configuration
Before modifying the code, ensure the MCP server can authenticate. The Mendix SDK requires a Personal Access Token (PAT).
1. **Generate PAT:** Log in to Mendix User Settings (developersettings), create a token with `mx:modelrepository:repo:read` scope.²⁴
2. **Configure Antigravity:** Open `mcp_config.json`.
3. **Inject Variable:**
```json
{
"mcpServers": {
"mendix-mcp": {
"command": "node",
"args": ["/Users/username/projects/mendix-mcp/build/index.js"],
"env": {
"MENDIX_TOKEN": "YOUR_GENERATED_PAT_STRING",
"MENDIX_USERNAME": "your.email@example.com"
}
}
}
}
```
*Note: For production or shared environments, use a .env file loaded by the script (dotenv) to avoid committing secrets to mcp_config.json.²⁶*
### 9.2 Step 2: Implement the DTO Mappers
Create a file `src/mappers.ts` in your MCP project.
```typescript
import { domainmodels } from "mendixmodelsdk";
export function mapEntity(entity: domainmodels.Entity) {
return {
_type: "Entity",
name: entity.name,
attributes: entity.attributes.map(attr => ({
name: attr.name,
type: attr.type.structureTypeName.split('$').pop(), // Clean up "DomainModels$String"
default: attr.value?.defaultValue
})),
documentation: entity.documentation
};
}
```
### 9.3 Step 3: Define the MCP Tool Handler
Update your main server file (e.g., `src/index.ts`) to use the mapper.
```typescript
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { MendixPlatformClient } from "mendixplatformsdk";
import { mapEntity } from "./mappers";
const server = new McpServer({ name: "mendix-mcp", version: "1.0.0" });
const client = new MendixPlatformClient();
server.tool(
"get_module_entities",
{ moduleName: z.string(), projectId: z.string(), branch: z.string() },
async ({ moduleName, projectId, branch }) => {
try {
// 1. Fetch Working Copy
const app = client.getApp(projectId);
const workingCopy = await app.createTemporaryWorkingCopy(branch);
const model = await workingCopy.openModel();
// 2. Find Domain Model
const domainModelInterface = model.allDomainModels()
.find(dm => dm.containerAsModule.name === moduleName);
if (!domainModelInterface) return { content: [{ type: "text", text: "Module not found" }] };
// 3. LOAD the unit (Critical Step)
const domainModel = await domainModelInterface.load();
// 4. Map to DTO (The Fix)
const entities = domainModel.entities.map(mapEntity);
// 5. Return Safe JSON
return {
content: [{ type: "text", text: JSON.stringify(entities, null, 2) }]
};
} catch (err: any) {
return { content: [{ type: "text", text: `Error: ${err.message}` }] };
}
}
);
// Start Server
const transport = new StdioServerTransport();
await server.connect(transport);
```
### 9.4 Step 4: Verify with MCP Inspector
Do not rely solely on Antigravity's error reports. Use the MCP Inspector to verify the output.²⁷
* Run: `npx @modelcontextprotocol/inspector node build/index.js`
* Invoke the tool `get_module_entities` manually.
* Confirm that the JSON output is clean, nested correctly, and contains no circular references.
## 10. Performance and Optimization: Lazy Loading
A naive implementation might try to load the entire application at once. Mendix apps can be massive. Loading 50 modules with 500 entities will:
1. Consume gigabytes of RAM (Node.js heap limit).
2. Take minutes to download from the Model Server.
3. Exceed the context window of the LLM (Gemini 3 Pro) with the JSON output.²⁸
### 10.1 Granular Tool Design
To support the Agentic workflow effectively, break the tools down into a hierarchy of discovery:
| Tool Name | Purpose | Output Size |
| :--- | :--- | :--- |
| `list_modules` | Discovery. Lists all module names. | Tiny (< 1KB) |
| `list_module_contents` | Structure. Lists entity/microflow names in a module. | Small (< 10KB) |
| `get_entity_details` | Detail. Returns attributes/associations for one entity. | Medium (~2KB) |
| `get_microflow_logic` | Logic. Returns the activity graph of a microflow. | Variable (Medium to Large) |
This approach allows the Agent to "Plan" its exploration. It first looks at the list of modules, decides which one is relevant (e.g., "Sales"), then lists its contents, and finally reads the specific entity it needs to modify. This mimics how a human developer navigates Studio Pro.
## 11. Security and Configuration
The integration of an AI agent with read/write access to a codebase requires strict security protocols.
### 11.1 Managing the Personal Access Token (PAT)
The Mendix PAT is a high-privilege credential. It grants access to the Model Repository, effectively allowing the bearer to download the intellectual property of the application or inject malicious code.²
* **Do not commit mcp_config.json** if it contains the token. Add it to `.gitignore`.
* **Scope Minimization:** When creating the PAT, select the minimum required scopes. If the agent is only reading, use `mx:modelrepository:repo:read`. Do not grant write access unless necessary.²⁴
* **Token Rotation:** Regularly rotate the PAT.
### 11.2 Antigravity Network Isolation
Antigravity's sandboxing may block network requests by default. The user must explicitly allow the MCP server to access external domains.
* **Domains to Whitelist:**
* `*.mendix.com` (Platform APIs)
* `*.home.mendix.com` (Team Server / Sprintr)
* `*.github.com` (If the model is hosted on GitHub)
If the user is in a corporate environment with a firewall, they may need to configure the MCP server to use a proxy agent. This can be done by passing `HTTP_PROXY` and `HTTPS_PROXY` in the `env` block of `mcp_config.json`.
## 12. Conclusion
The "wall" encountered by the user—the `TypeError: Converting circular structure to JSON`—is a deterministic consequence of exposing the raw, cyclic Mendix Metamodel to the acyclic serialization logic of the MCP JSON-RPC transport layer. It is not a bug in Antigravity or Mendix, but an integration mismatch.
The remediation strategy detailed in this report transforms the integration from a direct pipe to a curated API. By implementing Data Transfer Objects (DTOs), the MCP server acts as an intelligent mediator, sanitizing the complex internal state of the Mendix SDK into clean, semantic JSON schemas that the Google Antigravity agent can consume, reason about, and act upon.
Implementing this fix involves:
1. **Code Level:** Replacing direct return statements with DTO mapping functions (`serializeEntity`).
2. **Configuration Level:** Properly injecting the `MENDIX_TOKEN` via `mcp_config.json` to bypass environment isolation.
3. **Architectural Level:** Adopting a granular, lazy-loading toolset to respect memory and context limits.
By resolving this serialization bottleneck, the user enables the true potential of Agentic AI: an autonomous "pair programmer" capable of deeply understanding the structural relationships of a Low-Code application and assisting in complex refactoring or documentation tasks.
## 13. Future Outlook: Self-Correcting Applications
Looking beyond the immediate fix, this integration paves the way for "Self-Correcting" Mendix applications. Once the MCP server can reliably read the model, it can be extended to *write* to the model (using the SDK's transactional capabilities). An Antigravity agent could theoretically:
1. Read the "Error Review Report" from a runtime log (as provided by the user).
2. Analyze the stack trace.
3. Inspect the relevant Microflow via the MCP tool.
4. Identify the logic error.
5. Generate a fix script using the Mendix SDK.
6. Commit the fix to the Team Server.
This closes the loop between operations and development, moving from "Human-in-the-loop" to "Human-on-the-loop" oversight, fundamentally accelerating the lifecycle of Low-Code development.
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