List the AI sources currently registered on a project (attributes populated via set_ai_source). Returns {items:[{code, url, handle}]} — code is the attribute name (available as custom_ once applied), handle the product key it joins on. project_id is OPTIONAL (inferred for a single-project customer).
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint and idempotentHint annotations, the description documents the exact return shape {items:[{code, url, handle}]} and explains the meaning of each field, including the custom_<code> behavior and join semantics of handle. It also discloses that project_id may be inferred, adding valuable runtime behavior not present in annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the core action. Every sentence adds necessary information: the purpose, the response contract with field semantics, and the only parameter's optionality. There is no wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list operation with one optional parameter and no output schema, the description is complete. It gives the return format, explains what code and handle mean, and clarifies project_id behavior, so an agent has everything needed to call the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema provides only the integer type and exclusiveMinimum for project_id, with 0% schema description coverage. The description compensates fully by stating that project_id is optional and inferred for single-project customers, which is essential for correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: 'List the AI sources currently registered on a project'. It ties the data to set_ai_source, which helps distinguish from set/delete_ai_source, but it does not explicitly contrast with list_api_sources, leaving some sibling differentiation to the tool names.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: to see AI sources that were previously populated via set_ai_source. It also clarifies that project_id is optional and inferred for single-project customers. However, it does not explicitly state when to prefer this over related list tools such as list_api_sources or list_source_attributes.
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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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.
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.
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.
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.
