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bluebonnet-sales

bluebonnet-sales

by hostedinthebarn
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

sales_summary

Read-only

Generates revenue, order count and average order value for a specified date range, excluding cancelled orders, so you can review sales performance over any period.

Instructions

Revenue, order count and average order value between two dates (YYYY-MM-DD, inclusive).

Cancelled orders are left out.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "start": "<start>",
      "end": "<end>"
    },
    "name": "sales_summary"
  }
}

Input Schema

NameRequiredDescriptionDefault
endYes
startYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and closed-world, so the description's job is to add domain rules — and it does: cancelled orders are excluded and the range is inclusive. It stops short of stating which date field the range applies to (order date vs. payment date) or the timezone/currency basis.

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?

Two short sentences, metrics and range stated first, the exclusion rule second. Nothing is padded and no sentence is wasted.

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?

With no output schema, the description should say what comes back (field names, currency, whether averages are per-order or per-line). For a simple two-parameter read tool it is otherwise adequate, but the return shape is left entirely to inference.

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

Parameters4/5

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

Schema coverage is 0%, so the description carries the burden, and it does supply the essential format (YYYY-MM-DD) and inclusivity of the end date. It leaves timezone handling and the mapping of start/end to the underlying date field unspecified.

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?

Names the exact metrics computed (revenue, order count, average order value) and the scoping dimension (date range), which separates it from siblings like top_customers or low_stock. It lacks an explicit verb, but the intent is unambiguous for an aggregation tool.

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?

There is no statement of when to use this instead of run_query or top_customers, and no prerequisites or exclusions beyond the cancelled-orders note. The agent must infer that this is the canned sales-metrics path rather than a general query.

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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