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mcp-server

by com.koongo

get_rule

Read-only

Get the full definition of one product rule (Attribute Rule), including its verbatim rules[] (round-trippable — you can edit it and pass it back to update_rule). Returns {ruleId, name, description, category, scope, rules, createdAt, updatedAt}. rules[] is {sortId, enabled, type, conditions, operationGroups:[{mode, parentMode, attributes, operations:[{name, arguments}]}]} (see discover_rule_operations). scope selects the library: 'project' (default) or 'shared_template'. An unknown rule_id returns error 'not_found'. rule_id is from list_rules. project_id is OPTIONAL (inferred for a single-project customer; project_id_required otherwise — then call list_projects).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoWhich library to read from: 'project' (default) = the project's own rules; 'shared_template' = the reusable shared template library.
rule_idYes
project_idNo

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description reveals the exact return shape, the round-trippable nature of rules[], the 'not_found' error for unknown rule_id, and the conditional behavior for project_id. These are important runtime behaviors an agent cannot infer from annotations alone.

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?

Every sentence adds information: primary purpose, return contract, nested structure, scope semantics, error behavior, and parameter provenance. It is dense but structured, and the critical round-trip concept is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema, the description provides the complete return shape and nested type details. It also covers error cases, parameter disambiguation, and relationships to sibling tools, leaving little ambiguity for an agent to invoke the tool correctly.

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 coverage is only 33%, but the description compensates by explaining all three parameters: rule_id originates from list_rules, scope selects the library with a default, and project_id is optional in some cases but required in others, with a pointer to list_projects. This is exactly the semantic grounding the schema lacks.

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 states a specific verb and resource: 'Get the full definition of one product rule (Attribute Rule)'. It distinguishes itself from sibling tools like list_rules (full vs. summary), update_rule (round-trippable), and delete_rule by emphasizing the read-only retrieval and the exact return payload.

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?

It gives clear context on how rule_id relates to list_rules, how the returned rule feeds update_rule, and when project_id must be supplied (by referencing list_projects). It does not explicitly spell out 'do not use this to list rules', but the phrase 'one product rule' and the round-trip workflow imply the boundary sufficiently.

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

A4.2/5.0
Disambiguation4/5

Tools are organized around distinct resources (ads, marketplaces, feeds, orders, rules, sources) with clear action verbs, and descriptions explicitly disambiguate near-pairs like get_feed_status vs ad_status or set_feed_filter vs set_feed_attribute_filter. A few similarly named status/action pairs (e.g. ad_status vs get_ad, run_ad_item_action vs run_ad_operation) require careful reading, but overall the purposes are separable.

Naming Consistency4/5

The overwhelming majority follow a consistent verb_noun snake_case pattern (list_*, get_*, create_*, set_*, run_*, test_*). Minor deviations like ad_status and marketplace_status (noun-based status tools) and koongo_knowledge break the pattern slightly, but the convention is clearly recognizable and predictable.

Tool Count1/5

At 105 tools, the surface is extreme and far beyond the 50+ threshold, even for a complex e-commerce integration domain. Much of the bloat comes from systematic triplication across ads, marketplaces, and feeds (e.g. three nearly identical map_*_attribute tools, three list_*_items, three get_*_report) that a generic resource parameter could have consolidated.

Completeness4/5

The toolset covers the full lifecycle of feeds, ads, marketplaces, order connections, rules, and imports, including create/read/update/delete, status monitoring, item-level actions, validation, repair, and restore. Minor gaps exist, such as no delete for standalone order connections and limited update capabilities for some entities, but these are workable and do not create dead ends for the core workflows.

Resources