mcp-server

set_category_mapping

DestructiveIdempotent

Map store categories to marketplace/taxonomy categories. Address the taxonomy by feed_id OR explicitly by taxonomy_code (+ optional taxonomy_locale). rules is a list of {store_category_ids:[...], target_hash} — each maps those store categories (ids from list_store_categories / list_unmapped_categories) to one target (hash from search_target_categories); a store category already mapped is re-pointed to the new target. The rule set is SHARED per project+taxonomy+locale, so if OTHER feeds use this taxonomy the call returns status=mapping_exists_requires_confirm (HTTP 409) with affectedFeedIds — re-call with confirm:true to apply. SAVE-ONLY: persists but does NOT regenerate — call export_feed afterwards. Returns {taxonomyCode, taxonomyLocale, status, rulesWritten, reason, affectedFeedIds}; status is 'updated' | 'no_changes' | 'mapping_exists_requires_confirm' | 'rejected' (reason: no_taxonomy | unknown_target | unknown_store_category | empty_rules). project_id is OPTIONAL (inferred for a single-project customer).

Input Schema

TableJSON Schema

Name	Required	Description
`rules`	Yes
`confirm`	No	Set true to apply when other feeds share this taxonomy (status=mapping_exists_requires_confirm); default false.
`feed_id`	No
`project_id`	No
`taxonomy_code`	No	Target taxonomy id (e.g. 'google'); alternative to feed_id for addressing the taxonomy.
`taxonomy_locale`	No	Locale of the taxonomy tree, e.g. 'en_GB' — defaults to the taxonomy's own default when omitted.

TDQS

A4.8/5.0

Behavior5/5

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

The description adds substantial behavioral detail beyond the annotations: the shared rule set per project+taxonomy+locale, the HTTP 409 conflict requiring confirm:true, re-pointing of already mapped categories, save-only persistence without regeneration, and the full status/reason vocabulary. This is exactly the kind of context annotations cannot convey.

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?

The description is dense but every sentence carries essential operational information: addressing, rule structure, sharing/conflict behavior, save-only caveat, and return statuses. It is front-loaded with the core purpose and structured logically, so the length is justified by the tool's complexity.

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?

The description is complete for a tool of this complexity. It covers all parameter semantics, the conflict/confirm flow, post-call requirements (export_feed), and the exact return status values despite the absence of an output schema. An agent has everything needed to invoke it 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?

With only 50% schema description coverage, the description compensates fully: it explains the rules structure (store_category_ids and target_hash), the origin of IDs, feed_id vs taxonomy_code addressing, the optional project_id inference, and confirm semantics. Even for parameters that have schema descriptions (confirm, taxonomy_code, taxonomy_locale), the description integrates them into a coherent workflow.

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 clearly states the operation: 'Map store categories to marketplace/taxonomy categories.' It distinguishes itself from siblings by referencing helper tools (list_store_categories, search_target_categories) and export_feed, and by explaining the two addressing modes (feed_id vs taxonomy_code). This leaves no ambiguity about what the tool does.

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?

The description gives rich usage context: it tells the agent where to source store_category_ids and target_hash, how to address the taxonomy, and that export_feed must be called afterwards. It does not explicitly say 'use get_category_mapping to read existing mappings' or provide any when-not-to-use exclusions, but the workflow is clearly implied.

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

All mcp-server tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?