| Name | Required | Description | Default |
|---|---|---|---|
| geo | No | ISO 3166-1 alpha-2 country code. Matches slots whose publisher-declared geo_targets contain this market (case-insensitive). | |
| slot_type | No | Filter by slot type | |
| max_results | No | Maximum results (default: 20) | |
| publisher_id | No | Filter by publisher UUID | |
| pricing_model | No | Filter by pricing model |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is clear. The description adds behavioral context by stating it returns 'available' slots and describing the output content (name, type, floor price, pricing models, capacity, geo targets, status). It also notes filtering by publisher_id, slot_type, pricing_model, or geo, which goes beyond the 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 well-structured with clear sections (tool_description, when_to_use, combination_hints, output_format). Every section is relevant and adds value: the tool description is one sentence, when_to_use is two short lines, combination_hints gives workflow, and output_format lists return fields. No wasted words.
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?
With 5 optional params fully documented in the schema, no output schema, and a straightforward list operation, the description provides a complete picture: what it lists, when to use, how it fits into the workflow, and what the response includes. No critical information is missing.
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?
Schema description coverage is 100% with detailed parameter descriptions (e.g., geo field explains ISO code and matching behavior). The description only repeats 'Filter by publisher_id, slot_type, pricing_model, or geo' without adding new semantic meaning. Per baseline, when schema covers all parameters, a score of 3 is appropriate.
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 explicitly states 'List available publisher inventory slots for programmatic media buying' with a specific verb and resource. It clearly distinguishes from siblings like get_inventory_item by focusing on listing all slots vs. single-item detail, and names the output (ad slots with pricing, rules, capacity).
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 'when_to_use' section explicitly positions this tool 'Before create_media_buy' and for 'browse publisher ad inventory for campaign planning.' The combination_hints further clarify the flow: list_inventory → get_inventory_item → create_media_buy, and mention filtering options. This is strong contextual guidance.
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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Most tools have distinct purposes, but there is some overlap between get_product and nexbid_product, which are explicitly described as aliases, and between list_products and nexbid_search with content_type='product'. This could cause confusion, though descriptions help clarify. Other tools like activate, pause, and cancel are well-differentiated for media buy lifecycle management.
The naming is mixed with no consistent pattern. Some tools use verb_noun (e.g., create_media_buy, list_inventory), others use noun_verb (e.g., nexbid_search, nexbid_purchase), and some are single verbs (e.g., activate, pause, cancel). While readable, the lack of a uniform convention across the set reduces predictability.
With 19 tools, the count is on the higher side but reasonable for the dual domains of media buying and marketplace discovery. It covers operations like listing, creating, managing, and reporting, which justifies the number. However, it borders on feeling heavy, especially with overlapping tools like get_product and nexbid_product.
The tool set provides comprehensive coverage for both media buying (create, submit, activate, pause, cancel, track, settle, report, compliance) and marketplace discovery (search, categories, product details, purchase, order status). There are no obvious gaps; workflows are well-supported with clear combination hints, ensuring agents can handle end-to-end tasks without dead ends.