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marketbasketanalysis-mcp

by io.github.48x-ai

get_forecast_alerts

For an inventory or merchant-ops agent: list forecast-based alerts, the bundles with stockout risk, demand drop, demand spike, or an unreliable forecast curve. Use this when a merchant asks 'what's at risk of stockout?', 'which bundles are losing demand?', 'do I need to reorder anything?', or 'what should I restock?'. Pair with forecast_bundle to drill into a specific bundle.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNoFilter by alert kind. Default 'all'.all
limitNoMax alerts to return. Default 10, max 50.
severityNoFilter by severity. Default 'all'.all

TDQS

A4.4/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It states 'list', implying a read-only operation, and clarifies the data source is forecast-based, but it does not explicitly say there are no side effects, describe output shape, or mention ordering/pagination behavior. It is safe but not deeply transparent.

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?

Three sentences, front-loaded with purpose, followed by use cases and a drill-down suggestion. Every sentence earns its place; no filler or repetition of schema details.

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?

For a moderate-complexity list tool with 3 optional parameters and no output schema, the description gives enough context to select and invoke it correctly: alert kinds, merchant intents, and a companion tool. It does not describe the return shape or alert fields, but that is not strictly required for invocation and no output schema exists to contradict it.

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 100%, so parameters are already documented, but the description adds value by mapping the kind enum to plain-language alert categories (stockout risk, demand drop, demand spike, unreliable forecast curve) and linking them to merchant questions. Severity and limit are left to the schema, which is sufficient.

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

Purpose5/5

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

The description opens with a specific verb and resource: 'list forecast-based alerts', and enumerates the alert kinds (stockout risk, demand drop, demand spike, unreliable forecast curve). This clearly distinguishes it from sibling tools like get_drift_alerts or get_recommendations by emphasizing 'forecast-based' alerts for bundles.

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

Usage Guidelines5/5

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

Gives explicit merchant-question triggers ('what's at risk of stockout?', 'which bundles are losing demand?', 'do I need to reorder anything?', 'what should I restock?') and names a complementary tool, forecast_bundle, for drilling in. This is direct, actionable guidance for when to use the 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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TDQS

A4.1/5.0
Disambiguation4/5

Tools map largely one-to-one to distinct actions, and descriptions explicitly differentiate similar-looking pairs like get_recommendations vs. get_bundle_for_cart vs. find_substitutes. However, score_cross_sell vs. analyze_basket and get_rationale vs. explain_opportunity could still confuse an agent, since both score/explain overlapping concepts. Overall ambiguity is low but not zero.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: get_* for retrieval, explain_* for drill-downs, score_* for validations, and action verbs for state changes. No camelCase or mixed conventions are present, making the naming highly predictable.

Tool Count3/5

19 tools is on the heavy side for a single server and exceeds the typical 3–15 range, even though most tools have distinct purposes. The large count reflects a broad feature set covering recommendations, alerts, forecasting, reorder, returns, and weekly planning, but a few tools could be consolidated. It feels slightly bloated rather than egregiously so.

Completeness4/5

The surface covers the main discovery–evaluation–action workflow: recommend, score, explain, triage, plan, and forecast. Minor gaps exist—there is no direct tool for creating or editing a bundle outside of weekly-plan actions, and no catalog-browsing capability—but those are largely external concerns. The workflow is coherent with no major dead ends.

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