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Find products in a synthetic retail catalog by matching a query as a case-insensitive substring of names, descriptions, categories, or brands.

Instructions

Search the synthetic catalog (about 500 products).

Example queries that return rows: headphones, laptop, coffee, yoga, tv, jacket. Matching is a case-insensitive substring of name, description, category, or brand.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSubstring matched against name, description, category, and brand. Try headphones, laptop, coffee, yoga, or tv. A random word often returns [].

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the matching algorithm, the searched fields, and the catalog size. It says nothing about result limits, ordering/ranking, or pagination behavior, which matter for predicting what a search returns; the 'random word often returns []' hint lives in the schema rather than the description.

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 plus an example line, front-loaded with the resource and its size. The example-query line partly duplicates the parameter description, which is the only real redundancy.

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 single-parameter search tool with an output schema covering return values, the description gives enough to call it correctly: what is searched, how matching works, and what kinds of terms succeed. The remaining gap is result-set behavior (limits, ordering), which is a minor omission rather than a blocking one.

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 description coverage is 100%, so the sole parameter is fully documented in the schema, and the description largely repeats it (matching fields, example queries). No new syntax, limits, or formatting constraints are added beyond what the schema already provides, so the baseline of 3 applies.

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 states a specific verb and resource (search a ~500-product catalog) and spells out exactly what is matched (case-insensitive substring of name, description, category, brand). It does not name any sibling tool, but its scope is clearly distinct from get_product_details and check_inventory, so an agent can place it without opening the schema.

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?

Usage is only implied: you search to discover products. There is no explicit when-to-use/when-not guidance and no routing to alternatives such as switching to get_product_details once an ID is known. The example queries are practical hints but not selection guidance.

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