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mcp-server

by com.koongo

repair_ad

Repair an ad — rebuild its internal profiles, clear its cache and re-export. Ads are ads-based channels (advertising / price-comparison / classifieds), not marketplaces: they publish products to the ad platform and have no order sync. Use this when an ad is in a broken or inconsistent state. Returns {integrationId, action:'repair', status:'repaired', kind:'ads', message}. ad_id is from list_ads. project_id is OPTIONAL (inferred for a single-project customer; project_id_required otherwise — then call list_projects).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ad_idYes
project_idNo

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, so the agent knows it mutates. The description adds specifics of what the repair does (rebuild profiles, clear cache, re-export) and the exact return shape. It does not discuss potential side-effects like reversibility or failure modes, but the destructiveHint=false annotation mitigates that. Overall, it adds value beyond annotations without contradicting them.

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 a single compact paragraph, front-loaded with the action and then the use-case, output format, and parameter notes. Every sentence earns its place; no filler. It is well-structured and easy to scan.

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?

Despite no output schema, the description explicitly states the return value ({integrationId, action:'repair', status:'repaired', kind:'ads', message}). Both parameters are fully documented, the use case is clear, and the ad-vs-marketplace distinction is covered. Nothing essential is missing for an agent to correctly invoke this tool.

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%, so the description must compensate. It fully explains ad_id ('from list_ads') and project_id (optional vs. required, with inference rule and fallback to list_projects). This is far more than the schema provides, giving the agent precise parameter semantics.

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 ('repair') and resource ('an ad'), then explains what the repair entails: rebuild profiles, clear cache, and re-export. It explicitly differentiates ads from marketplaces, which is important given sibling tools like repair_marketplace. The use case ('broken or inconsistent state') further clarifies purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says 'Use this when an ad is in a broken or inconsistent state,' which gives a clear trigger. It also contrasts vs. marketplaces (not order sync) and provides conditions for project_id: optional for single-project customers, required otherwise (then call list_projects). This is actionable guidance beyond any schema.

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