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run_scenario_8632

Execute Scenario D - Subscenario by sending text input, enabling AI systems to trigger and interact with automation workflows on the Make MCP Server.

Instructions

Scenario D - Subscenario

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNo

Implementation Reference

  • Executes the 'run_scenario_8632' tool by parsing the scenario ID from the tool name, calling make.scenarios.run(8632, arguments), formatting the outputs as JSON text response, or error message.
    server.setRequestHandler(CallToolRequestSchema, async request => {
        if (/^run_scenario_\d+$/.test(request.params.name)) {
            try {
                const output = (
                    await make.scenarios.run(parseInt(request.params.name.substring(13)), request.params.arguments)
                ).outputs;
    
                return {
                    content: [
                        {
                            type: 'text',
                            text: output ? JSON.stringify(output, null, 2) : 'Scenario executed successfully.',
                        },
                    ],
                };
            } catch (err: unknown) {
                return {
                    isError: true,
                    content: [
                        {
                            type: 'text',
                            text: String(err),
                        },
                    ],
                };
            }
        }
        throw new Error(`Unknown tool: ${request.params.name}`);
    });
  • src/index.ts:37-57 (registration)
    Dynamically registers the 'run_scenario_8632' tool (and others) by listing on-demand scenarios from the Make API, generating name, description, and inputSchema for scenario ID 8632 if present.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
        const scenarios = await make.scenarios.list(teamId);
        return {
            tools: await Promise.all(
                scenarios
                    .filter(scenario => scenario.scheduling.type === 'on-demand')
                    .map(async scenario => {
                        const inputs = (await make.scenarios.interface(scenario.id)).input;
                        return {
                            name: `run_scenario_${scenario.id}`,
                            description: scenario.name + (scenario.description ? ` (${scenario.description})` : ''),
                            inputSchema: remap({
                                name: 'wrapper',
                                type: 'collection',
                                spec: inputs,
                            }),
                        };
                    }),
            ),
        };
    });
  • Converts Make scenario input specification to JSON Schema object used for the tool's inputSchema validation.
    export function remap(field: Input): unknown {
        switch (field.type) {
            case 'collection':
                const required: string[] = [];
                const properties: unknown = (Array.isArray(field.spec) ? field.spec : []).reduce((object, subField) => {
                    if (!subField.name) return object;
                    if (subField.required) required.push(subField.name);
    
                    return Object.defineProperty(object, subField.name, {
                        enumerable: true,
                        value: remap(subField),
                    });
                }, {});
    
                return {
                    type: 'object',
                    description: noEmpty(field.help),
                    properties,
                    required,
                };
            case 'array':
                return {
                    type: 'array',
                    description: noEmpty(field.help),
                    items:
                        field.spec &&
                        remap(
                            Array.isArray(field.spec)
                                ? {
                                      type: 'collection',
                                      spec: field.spec,
                                  }
                                : field.spec,
                        ),
                };
            case 'select':
                return {
                    type: 'string',
                    description: noEmpty(field.help),
                    enum: (field.options || []).map(option => option.value),
                };
            default:
                return {
                    type: PRIMITIVE_TYPE_MAP[field.type as keyof typeof PRIMITIVE_TYPE_MAP],
                    default: field.default != '' && field.default != null ? field.default : undefined,
                    description: noEmpty(field.help),
                };
        }
    }
  • Core helper method invoked to run scenario 8632 by posting input arguments to the Make API endpoint `/scenarios/8632/run`.
    async run(scenarioId: number, body: unknown): Promise<ScenarioRunServerResponse> {
        return await this.#fetch<ScenarioRunServerResponse>(`/scenarios/${scenarioId}/run`, {
            method: 'POST',
            body: JSON.stringify({ data: body, responsive: true }),
            headers: {
                'content-type': 'application/json',
            },
        });
    }
Behavior1/5

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. However, it offers no information about what the tool does (e.g., whether it runs a simulation, processes data, or performs another action), its effects, permissions needed, or any constraints like rate limits.

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

Conciseness2/5

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

While concise with only three words, the description is under-specified rather than efficiently informative. It fails to convey essential details, making brevity a detriment rather than a strength in this context.

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

Completeness1/5

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

Given the lack of annotations, no output schema, and a parameter with 0% schema coverage, the description is completely inadequate. It does not compensate for these gaps, failing to provide any meaningful context about the tool's purpose, usage, or behavior.

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

Parameters1/5

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

The input schema has one parameter ('text') with 0% description coverage, and the tool description adds no meaning about this parameter. It does not explain what 'text' represents, its format, or how it influences the tool's behavior, leaving the parameter entirely undocumented.

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

Purpose2/5

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

The description 'Scenario D - Subscenario' is vague and tautological—it restates the tool name 'run_scenario_8632' without specifying what the tool actually does. It lacks a clear verb and resource, failing to distinguish this tool from its siblings (e.g., run_scenario_11422, run_scenario_11652).

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

Usage Guidelines1/5

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

No guidance is provided on when to use this tool versus its sibling tools. The description does not mention any context, prerequisites, or alternatives, leaving the agent with no information to make an informed selection among similar-named tools.

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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