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MealCP MCP

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by MealCP
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Python
Remote

search_products

Find grocery products by keyword or filters like country, retailer, brand, price, and category. Get matching items with prices, URLs, and facet counts.

Instructions

Search the grocery product catalog by keyword with faceted filtering.

Returns matching products with name, brand, price, unit price, retailer and a URL, plus facet counts for retailers, brands and countries.

Args: q: Free-text query (e.g. "potatoes", "krumpli"). Empty returns all, sorted by sort or recency. country: ISO 3166-1 alpha-2 country code (e.g. HU, FR). retailer: Retailer slug (e.g. tesco-hu, auchan-fr). chain: Chain slug (e.g. tesco, auchan). brand: Brand free-text token. tag: Tag slug (e.g. vegan). category: Category slug at any level (L1/L2/L3, e.g. fresh-produce, dairy-eggs, vegetables/potatoes); matches the whole subtree. city: City free-text token filtering stores carrying the product. currency: ISO 4217 currency code (e.g. HUF, EUR). min_price: Minimum latest price (inclusive). max_price: Maximum latest price (inclusive). sort: Stable sort token: relevance, newest, oldest, price_asc, price_desc, unit_price_asc, unit_price_desc. page: 1-indexed result page. page_size: Hits per page (1-100).

Returns: A structured search response.

Raises: mcp.server.mcpserver.exceptions.ToolError: If the search API is unavailable; the message starts with the API's stable error token.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNo
tagNo
cityNo
pageNo
sortNo
brandNo
chainNo
countryNo
categoryNo
currencyNo
retailerNo
max_priceNo
min_priceNo
page_sizeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hitsYes
pageYes
foundYes
queryYes
facetsYes
page_sizeYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.1.1
    • changedInput schema / properties / category / anyOf
      Previous value: -[
-  {
-    "maxLength": 128,
-    "pattern": "^[A-Za-z0-9_-]+$",
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "maxLength": 128,
+    "pattern": "^[A-Za-z0-9_-]+(/[A-Za-z0-9_-]+)*$",
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
  2. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden, and it does add real behavioral detail: returned fields, facet counts, and a Raises clause naming ToolError with a stable error token prefix. It still omits auth requirements, rate limits, or cost characteristics that an unannotated tool would ideally disclose.

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 summary sentence followed by cleanly separated Args/Returns/Raises blocks; with 14 parameters the length is justified and each arg line earns its place. The 'Returns: A structured search response.' line is slightly vacuous given an output schema exists.

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 14-parameter search tool with an output schema, the description supplies everything needed to invoke it correctly: all parameter semantics, error behavior, and result composition. Only the absence of routing guidance relative to the sibling keeps it from being fully complete.

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

Parameters5/5

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

Schema description coverage is 0% for 14 parameters, so the description must compensate, and it does thoroughly: format examples (HU, FR; HUF, EUR), slug hierarchies with subtree matching for category, inclusive bounds for min_price/max_price, stable sort token semantics, 1-indexed paging and page_size 1-100. This adds meaning no schema field conveys.

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 ('Search the grocery product catalog by keyword') plus the faceting capability, so the agent knows exactly what it does. It does not, however, distinguish itself from the sibling get_price_history, leaving the agent to infer the split between catalog search and price history.

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 explicit when-to-use guidance and no mention of the sibling get_price_history. The only hint ('Empty returns all, sorted by sort or recency') is parameter behavior rather than a usage rule, so the agent must guess which tool fits a price-over-time question.

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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