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ConsignCloud MCP Server

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get_sales_trends

Analyze sales performance over time by retrieving trend data for specified date ranges and time intervals to support business decisions.

Instructions

Get sales trends and analytics

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_dateYesStart date (ISO 8601: YYYY-MM-DD) - REQUIRED
end_dateYesEnd date (ISO 8601: YYYY-MM-DD) - REQUIRED
bucket_sizeYesTime bucket size - REQUIRED

Implementation Reference

  • The core handler implementation for the 'get_sales_trends' tool. This method in the ConsignCloudClient class makes an HTTP GET request to the '/trends/sales' API endpoint with the provided parameters (start_date, end_date, bucket_size) and returns the response data.
    async getSalesTrends(params?: {
      start_date: string;
      end_date: string;
      bucket_size: 'day' | 'week' | 'month';
    }): Promise<any> {
      const response = await this.client.get('/trends/sales', { params });
      return response.data;
    }
  • src/server.ts:277-289 (registration)
    Registration of the 'get_sales_trends' tool in the createTools() function, including name, description, and input schema. This defines the tool for the MCP server.
    {
      name: 'get_sales_trends',
      description: 'Get sales trends and analytics',
      inputSchema: {
        type: 'object',
        properties: {
          start_date: { type: 'string', description: 'Start date (ISO 8601: YYYY-MM-DD) - REQUIRED' },
          end_date: { type: 'string', description: 'End date (ISO 8601: YYYY-MM-DD) - REQUIRED' },
          bucket_size: { type: 'string', enum: ['day', 'week', 'month'], description: 'Time bucket size - REQUIRED' },
        },
        required: ['start_date', 'end_date', 'bucket_size'],
      },
    },
  • Input schema definition for validating parameters of the 'get_sales_trends' tool.
    inputSchema: {
      type: 'object',
      properties: {
        start_date: { type: 'string', description: 'Start date (ISO 8601: YYYY-MM-DD) - REQUIRED' },
        end_date: { type: 'string', description: 'End date (ISO 8601: YYYY-MM-DD) - REQUIRED' },
        bucket_size: { type: 'string', enum: ['day', 'week', 'month'], description: 'Time bucket size - REQUIRED' },
      },
      required: ['start_date', 'end_date', 'bucket_size'],
    },
  • MCP server request handler dispatch for 'get_sales_trends', which calls the client.getSalesTrends method and formats the response as MCP content.
    case 'get_sales_trends':
      return { content: [{ type: 'text', text: JSON.stringify(await client.getSalesTrends(args as any), null, 2) }] };
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. 'Get sales trends and analytics' implies a read-only operation but doesn't specify authentication needs, rate limits, data freshness, or what the output looks like (e.g., aggregated data, charts). This is inadequate for a tool with no annotation coverage.

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 ('Get sales trends and analytics') with zero wasted words. It's front-loaded and efficiently communicates the core purpose in a single phrase, though this conciseness comes at the cost of detail.

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 tool with no annotations, no output schema, and three required parameters, the description is incomplete. It doesn't explain what 'trends and analytics' entail, how results are structured, or any behavioral constraints. Given the complexity implied by analytics and the lack of structured data, more context is needed.

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?

Schema description coverage is 100%, with all three parameters (start_date, end_date, bucket_size) clearly documented in the schema. The description adds no additional parameter information beyond what's in the schema, so it meets the baseline for high schema coverage without compensating value.

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

Purpose3/5

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

The description 'Get sales trends and analytics' states a clear verb ('Get') and resource ('sales trends and analytics'), but it's vague about what specific analytics are provided and doesn't differentiate from sibling tools like 'calculate_sales_totals' or 'get_sales'. It provides basic purpose but lacks specificity.

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?

No guidance is provided on when to use this tool versus alternatives like 'calculate_sales_totals' or 'list_sales'. The description doesn't mention context, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

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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