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EGorsel

Mendix Context Bridge

by EGorsel

get_domain_model

Retrieve entity and type overviews from Mendix modules to understand project structure and data models for development and analysis.

Instructions

Geeft een overzicht van alle entiteiten en types in een specifieke module.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
module_nameYesDe naam van de module waarvan je het domeinmodel wilt zien.

Implementation Reference

  • MCP CallTool request handler for the 'get_domain_model' tool. Extracts the 'module_name' parameter and delegates to MprReader.getDomainModel()
    if (request.params.name === "get_domain_model") {
        const moduleName = String(request.params.arguments?.module_name);
        const data = reader.getDomainModel(moduleName);
        return {
            content: [{ type: "text", text: JSON.stringify(data, null, 2) }]
        };
    }
  • src/server.ts:68-81 (registration)
    Registration of the 'get_domain_model' tool in the ListTools response, including name, description, and input schema requiring 'module_name'
    {
        name: "get_domain_model",
        description: "Geeft een overzicht van alle entiteiten en types in een specifieke module.",
        inputSchema: {
            type: "object",
            properties: {
                module_name: {
                    type: "string",
                    description: "De naam van de module waarvan je het domeinmodel wilt zien."
                }
            },
            required: ["module_name"]
        }
    },
  • Core implementation of getDomainModel in MprReader class: queries the 'Unit' table (limited to 20 rows) from the MPR SQLite database, optionally filtered by moduleName (placeholder logic).
    getDomainModel(moduleName?: string): any[] {
        if (!this.db) {
            throw new Error('Database not connected.');
        }
        try {
            // Placeholder logic: 
            // If moduleName is provided, we would filter by that.
            console.log(`Getting domain model for module: ${moduleName || 'All'}`);
    
            const stmt = this.db.prepare("SELECT * FROM Unit LIMIT 20");
            return stmt.all();
        } catch (error) {
            console.error('Error getting domain model:', error);
            return [];
        }
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool provides an overview, implying a read-only operation, but doesn't clarify if it's safe, requires permissions, has rate limits, or what the output format might be (e.g., list, structured data). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence in Dutch that directly states the tool's purpose without any fluff or redundancy. It's front-loaded with the key action and resource, making it efficient and easy to parse. Every word earns its place, achieving optimal conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage guidelines, behavioral traits, or output expectations. Without annotations or an output schema, the agent might struggle with how to interpret results, but the simplicity of the tool means it's not critically incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the parameter 'module_name' well-documented as 'De naam van de module waarvan je het domeinmodel wilt zien' (The name of the module for which you want to see the domain model). The description adds no additional parameter details beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Geeft een overzicht van alle entiteiten en types in een specifieke module' (Provides an overview of all entities and types in a specific module). It specifies the verb ('geeft een overzicht van' - provides an overview of) and resource ('alle entiteiten en types' - all entities and types), making the action and target explicit. However, it doesn't distinguish this from sibling tools like 'inspect_database_schema' or 'list_local_modules', which might offer related functionality, so it doesn't reach the highest score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'inspect_database_schema' or 'list_local_modules', nor does it specify prerequisites, exclusions, or contextual cues for selection. The agent must infer usage based on the purpose alone, which is insufficient for optimal tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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