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jihopark

Grocery Logger MCP Server

by jihopark

query_spending

Query total grocery spending by vendor, category, or date range to analyze spending patterns and view category or vendor breakdowns.

Instructions

Query total spending with optional filters.

Use this tool to analyze spending patterns. Supports filtering by:

  • vendor: Store name (e.g., "Trader Joes", "Costco")

  • category: Category name (e.g., "produce", "dairy")

  • start_date: Start of date range (ISO format, e.g., "2025-01-01")

  • end_date: End of date range (ISO format, e.g., "2025-01-31")

Returns total spending, item count, and breakdown by category or vendor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
vendorNo
categoryNo
end_dateNo
start_dateNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses the return shape (total spending, item count, breakdown by category or vendor), which is genuinely useful since there is no output schema, but it omits any note on defaults, result limits, or whether filters combine with AND semantics.

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?

Front-loaded purpose sentence followed by a scannable bulleted filter list and a returns sentence. Every element earns its place; only the generic 'Use this tool to analyze spending patterns' line is slightly redundant.

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 read-only, four-parameter query tool with no annotations and no output schema, the description covers purpose, all filters, and the return shape. An agent has enough to invoke it; only matching semantics and default behavior remain unspecified.

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 description coverage is 0%, so the description must compensate and largely does: it names all four parameters, gives example values for vendor and category, and specifies ISO date format with examples for start_date/end_date. Remaining gap is whether vendor/category are exact or substring matches.

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?

States a specific verb and resource ('Query total spending') with scope (optional filters), which is clear on its own. It does not explicitly distinguish itself from siblings like query_item_price, but the resource difference is self-evident.

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

Usage Guidelines3/5

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

'Use this tool to analyze spending patterns' implies the analysis use case, and the filter list hints at scoping. However, there is no explicit when-not guidance and no routing to alternatives such as query_item_price or list_items_by_category.

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