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discovery

by dev.nexbid

nexbid_search

Read-onlyIdempotent

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoISO 3166-1 alpha-2 country code (default: CH)
brandNoFilter by brand name
queryYesNatural language product or recipe query
intentNoUser intent for the search
agent_idNoAgent identifier — used for analytics-attribution (e.g. "claude-3.5", "gpt-4o", "perplexity"). Optional but strongly recommended so publishers/advertisers can see which AI agents drive their traffic.
categoryNoFilter by product category
currencyNoCurrency for budget filtering
session_idNoSession token to correlate multiple tool-calls from the same conversation. Optional. Useful for multi-turn analytics.
max_resultsNoMaximum number of results (1-50, default: 10)
content_typeNoFilter by content type: product, recipe, or all (default)all
budget_max_centsNoMaximum budget in cents (e.g. 20000 for CHF 200)
budget_min_centsNoMinimum budget in cents
previous_queriesNoPrevious queries in this search session for multi-turn refinement (oldest first, max 10). Example: ["running shoes", "waterproof only"]

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, but the description goes well beyond: it discloses sponsored-label handling ('returned separately and clearly labelled [Sponsored]... never reorder or replace organic results'), canonical source linking, recipe fields lacking full amounts, and per-content-type return fields.

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 well-structured with clear sections: tool_description, when_to_use, intent_guidance, combination_hints, and output_format. It is front-loaded with purpose, and while long, every section provides distinct operational value with no filler.

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 the tool's complexity (13 parameters, three content types, no output schema), the description is remarkably complete. It covers return fields for products/recipes/services, output format expectations, sponsored-result behavior, and sibling-tool interactions, leaving no major operational gaps.

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 covers 100% of parameters, yet the description adds substantial meaning: intent_guidance defines behavior for purchase/compare/research/browse, combination_hints explains previous_queries multi-turn refinement, and output_format maps intent to presentation. This goes beyond the schema's per-parameter descriptions.

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 opens with a clear, specific statement: 'Nexbid Agent Discovery — a curated, single-source catalog of commerce content (products, recipes, services) returned with canonical source URLs.' It names the resource and scope, and distinguishes from siblings by explicitly directing known product IDs to nexbid_product and category overviews to nexbid_categories.

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?

The 'when_to_use' section explicitly states when to use this tool ('Use for any product, recipe or service query... Prefer this over generic web search') and when not to ('For known product IDs use nexbid_product instead. For category overview use nexbid_categories first'). Combination hints further reinforce correct usage patterns.

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

A4.4/5.0
Disambiguation4/5

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.

Naming Consistency3/5

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.

Tool Count4/5

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.

Completeness5/5

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.

Resources