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dsr-cyber

bizdata-mcp

by dsr-cyber

inventory_alerts

Identify active products that are out of stock, at or below reorder point, or running low based on days of cover, so you can reorder stock before sales are lost.

Instructions

Active products that are out of stock, at or below their reorder point, or running low.

Days of cover = stock on hand / average daily units sold over the last 30 days, measured up to the most recent order in the data. Products with no recent sales show no cover figure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
min_days_of_coverNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses the exact computation of days of cover and the edge-case behavior that products with no recent sales show no cover figure. However, it never states that this is a read-only operation, says nothing about ordering, limits, or whether inactive products are excluded beyond the single word 'Active'.

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

Conciseness4/5

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

Two short sentences, front-loaded with the qualifying conditions followed by the metric definition. Every sentence earns its place, though the metric definition could be tightened slightly.

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

Completeness4/5

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

An output schema exists, so return values need no explanation, and the tool is a simple single-parameter read. The qualifying criteria and metric semantics are covered; only the parameter's filter behavior and result ordering are left unstated.

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 coverage is 0% — min_days_of_cover is documented only as 'integer, default 14'. The description compensates by defining what 'days of cover' means and how it is computed, which is the parameter's unit, but it never explains how the threshold is applied (filter direction, inclusivity), leaving the parameter's effect on results ambiguous.

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 names the resource and the precise conditions that qualify a product (out of stock, at/below reorder point, running low), which is far more specific than the bare tool name. It lacks an explicit verb (e.g., 'Returns/Lists'), but no sibling (run_sql, sales_summary, top_customers, refund_rate) overlaps this domain, so differentiation is not a concern.

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 reach for this tool versus alternatives such as run_sql or sales_summary, and no prerequisites or exclusions are given. The reader must infer usage entirely from the returned-content description.

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