caddence

Get retailer offers for a product

get_product_offers

All current UK retailer offers for one product, cheapest first, with variant details (shaft, flex, hand, loft, condition). Each offer includes buy_url — a Caddence redirect link. Always present buy_url as the purchase link when the user wants to buy; never reconstruct or substitute retailer URLs.

Input Schema

TableJSON Schema

Name	Required	Description
`slug`	No	Alias for product — pass either
`context`	Yes	Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): "Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization."
`product`	No	Product slug (preferred, from search results) or numeric product id

TDQS

A3.8/5.0

Behavior3/5

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

Without annotations, the description discloses sorting (cheapest first), geographic scope (UK only), and the inclusion of variant details. It also reveals that offers are current (not historical). However, it does not mention pagination limits, rate limits, or whether offers are cached, leaving gaps in behavioral transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long and front-loads key information (scope, sorting, variant details). The second sentence on buy_url usage is essential but could be slightly more concise. Overall, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters, no output schema, and no annotations, the description could be more complete. It lacks information about response format, error cases (e.g., product not found), and whether historical offers are included. The buy_url guidance is helpful but doesn't fully compensate for missing behavioral details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description does not clarify the difference between slug and product parameters beyond what the schema already states. The context parameter is well-documented in the schema with detailed instructions, so the description adds minimal value here.

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 tool will return 'All current UK retailer offers for one product, cheapest first, with variant details'. It distinguishes itself from sibling tools by mentioning specific fields like shaft, flex, hand, loft, condition, and the inclusion of buy_url links, which no other sibling tool addresses.

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 provides explicit guidance on presenting buy_url as the purchase link and warns against reconstructing retailer URLs. However, it does not explicitly state when to use this tool versus alternatives like get_price_history or get_deal_intelligence for different offer contexts.

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

All caddence 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

Disambiguation5/5

Each tool has a clearly distinct purpose: catalog stats for validation, deal intelligence for price quality, market deals for best values, price history for trends, product offers for purchase links, and search for initial product discovery. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent 'get_' prefix except 'search_golf_products', which uses 'search_'—this is appropriate as it's a broader discovery action while the rest retrieve specific data. The pattern is predictable and logically distinct.

Tool Count5/5

6 tools is perfectly scoped for a golf price comparison server: entry search, catalog metadata, deal finder, price quality, history, and offers. Each tool addresses a distinct task without redundancy.

Completeness5/5

The tool surface covers the full search-to-purchase workflow: product discovery (search_golf_products), catalog validation (get_catalog_stats), deal exploration (get_market_deals, get_deal_intelligence), historical context (get_price_history), and purchase links (get_product_offers). No obvious gaps.

Resources

Need Help?