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LicenseSpring MCP Server

by stier1ba

Add Feature Consumption

add_feature_consumption

Increase consumption units for a specific feature tied to a license key, hardware ID, and product in the LicenseSpring MCP Server.

Instructions

Add consumption units to a specific feature

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
consumptionsYes
featureYes
hardware_idYes
license_keyYes
productYes

Implementation Reference

  • The main handler function for the 'add_feature_consumption' tool. It validates input (via schema), makes a POST request to the LicenseSpring API endpoint '/api/v4/add_feature_consumption', and returns the JSON response or an error message.
    }, async ({ license_key, hardware_id, product, feature, consumptions }) => {
      try {
        const response = await apiClient.post('/api/v4/add_feature_consumption', {
          license_key,
          hardware_id,
          product,
          feature,
          consumptions,
        });
        
        return {
          content: [{
            type: 'text',
            text: JSON.stringify(response.data, null, 2),
          }],
        };
      } catch (error) {
        return {
          content: [{
            type: 'text',
            text: `Error adding feature consumption: ${handleApiError(error)}`,
          }],
          isError: true,
        };
      }
    });
  • Zod-based input schema for validating the parameters required by the add_feature_consumption tool.
    inputSchema: {
      license_key: z.string().min(1, 'License key is required'),
      hardware_id: z.string().min(1, 'Hardware ID is required'),
      product: z.string().min(1, 'Product code is required'),
      feature: z.string().min(1, 'Feature code is required'),
      consumptions: z.number().min(1, 'Consumption units must be positive'),
    },
  • MCP server tool registration call, including title, description, input schema, and inline handler function.
    server.registerTool('add_feature_consumption', {
      title: 'Add Feature Consumption',
      description: 'Add consumption units to a specific feature',
      inputSchema: {
        license_key: z.string().min(1, 'License key is required'),
        hardware_id: z.string().min(1, 'Hardware ID is required'),
        product: z.string().min(1, 'Product code is required'),
        feature: z.string().min(1, 'Feature code is required'),
        consumptions: z.number().min(1, 'Consumption units must be positive'),
      },
    }, async ({ license_key, hardware_id, product, feature, consumptions }) => {
      try {
        const response = await apiClient.post('/api/v4/add_feature_consumption', {
          license_key,
          hardware_id,
          product,
          feature,
          consumptions,
        });
        
        return {
          content: [{
            type: 'text',
            text: JSON.stringify(response.data, null, 2),
          }],
        };
      } catch (error) {
        return {
          content: [{
            type: 'text',
            text: `Error adding feature consumption: ${handleApiError(error)}`,
          }],
          isError: true,
        };
      }
    });
  • TypeScript type definition for the AddFeatureConsumptionRequest interface, matching the tool's input parameters.
    export interface AddFeatureConsumptionRequest {
      license_key: string;
      hardware_id: string;
      product: string;
      feature: string;
      consumptions: number;
    }
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. 'Add consumption units' implies a write/mutation operation, but the description doesn't address permission requirements, whether this operation is reversible, rate limits, or what happens on success/failure. It provides minimal behavioral context beyond the basic action.

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 extremely concise at just 6 words, front-loading the core purpose without any wasted words. Every word contributes directly to communicating the tool's function.

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

Completeness2/5

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

For a 5-parameter mutation tool with no annotations and no output schema, the description is inadequate. It doesn't explain parameter relationships, expected outcomes, error conditions, or how this tool differs from similar sibling tools. The agent would struggle to use this tool correctly without additional context.

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

Parameters2/5

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

With 0% schema description coverage for 5 required parameters, the description provides no information about parameter meanings or usage. 'Add consumption units to a specific feature' gives high-level context but doesn't explain what 'consumptions', 'feature', 'hardware_id', 'license_key', or 'product' parameters represent or how they should be used together.

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 action ('Add consumption units') and target ('to a specific feature'), which provides a specific verb+resource combination. However, it doesn't distinguish this tool from the sibling 'add_consumption' tool, which appears to be a very similar operation based on naming alone.

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. With sibling tools like 'add_consumption' (likely similar functionality) and various license management tools, there's no indication of appropriate contexts, prerequisites, or exclusions for this specific tool.

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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