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marketbasketanalysis-mcp

by io.github.48x-ai

forecast_bundle

For an inventory, purchasing, or merchant-ops agent: forecast weekly sales and recommend a buy quantity for a specific bundle over a configurable horizon. Uses additive Holt-Winters on the bundle's stored historical sales (demand forecasting). Use this when the agent asks 'how many of bundle X should I order?', 'what should I stock for the next N weeks?', 'what's the demand outlook for bundle Y?', or 'forecast the next 8 weeks for the camera bundle'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bundle_idYesBundle identifier (the platform-specific bundle/kit id).
horizon_weeksNoForecast horizon in WEEKS. Default 8, range 1..52. The server converts this to days for the backend, so pass the number of weeks, not days.

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden, and it does well: it discloses the algorithm (additive Holt-Winters), the data source (stored historical sales), and the advisory nature ('recommend'), implying no order execution. It could be more explicit about side effects or output shape, but the key behavior is clear.

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?

The description is compact and front-loaded: purpose first, method second, usage triggers third. The four example queries are somewhat repetitive but still useful for agent routing, so it earns a 4 rather than a 5.

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 simple two-parameter tool with no output schema, the description gives enough context to select and call it correctly: it names the inputs, the method, the data source, and the intended output (forecast plus recommended buy quantity). Exact return structure is unspecified, but not critical for invocation.

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?

Input schema coverage is 100%, with strong descriptions for both bundle_id and horizon_weeks, including default, range, and week-vs-day conversion. The description adds only generic 'configurable horizon' context, so it does not need to compensate further.

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 names a specific verb and resource: forecast weekly sales and recommend a buy quantity for a specific bundle. It also gives concrete user-query examples, making the tool's scope unmistakable and distinguishing it from alert/opportunity siblings.

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

Usage Guidelines4/5

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

The description explicitly provides 'Use this when...' triggers and targets inventory/purchasing/merchant-ops agents. It does not name alternatives or give explicit when-not-to-use guidance, so it stops short of a 5.

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.

Resources