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search_rules

Search Amazon, Temu, or TikTok Shop rule packs by keyword to find matching banned-word, compliance, or claim rules with severity, rationale, and fix suggestions.

Instructions

Search inside a rule pack by keyword. Returns matching rules with id, severity, category, patterns, rationale and fix suggestion. Useful to answer "why was this flagged?" or "what words are banned for supplements on Temu?".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoKeyword to match against rule id, patterns, message and suggestion. Empty returns all rules.
platformYesRule pack to search. Available: amazon (amazon-us, market=US, 20 rules), temu (temu-global, market=GLOBAL, 15 rules), tiktok (tiktok-us, market=US, 15 rules)
severityNoFilter by severity. Defaults to ALL.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses the return shape (id, severity, category, patterns, rationale, fix suggestion) and implies a read-only lookup, but says nothing about authentication, pagination, result limits, or whether the search is substring/regex based.

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 tight sentences: purpose, return contents, then motivating use cases. The essential verb+resource is front-loaded and no sentence is 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?

There is no output schema, but the description compensates by listing the returned fields, and all three parameters are fully documented in the schema. Minor gaps around result limits and query matching semantics keep it from a 5.

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 query, platform and severity are already fully documented including enum values and defaults. The description adds a natural-language example that subtly clarifies the query parameter's intent but no syntax beyond the schema. Baseline 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?

States a specific verb and resource ('Search inside a rule pack by keyword') and enumerates the returned fields, so the agent knows exactly what the tool produces. It does not explicitly contrast itself with siblings like list_rule_packs or suggest_rewrite, which keeps it short of a 5.

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?

Gives concrete usage contexts ('why was this flagged?', 'what words are banned for supplements on Temu?') that tell the agent when this tool is the right call. It stops short of naming alternatives or exclusions, so no 5.

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