caddence

Get price history

get_price_history

Daily lowest/average UK price for one product over a trailing window, plus summary stats (historical low with date, high, trend direction). Optionally scoped to a variant configuration; if the variant series is too thin the backend falls back to product-level history (see scope field).

Input Schema

TableJSON Schema

Name	Required	Description
`days`	No	Trailing window in days
`flex`	No
`hand`	No	e.g. "RH" or "LH"
`loft`	No
`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
`condition`	No

TDQS

B3.4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behaviors: trailing window, optional variant scoping, and backend fallback to product-level history when variant data is thin. This adds valuable transparency beyond the schema, though it omits details like error handling or authentication.

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

Conciseness3/5

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

The description is a single sentence covering main output, optional scoping, and fallback. It is concise but contains a misleading reference to 'scope field' that does not exist in the input schema, which harms clarity. Front-loading is adequate.

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

Completeness2/5

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

The description is insufficient for an 8-parameter tool with no output schema and no annotations. It does not explain the output format, error conditions, or the meaning of parameters like condition or days. The summary stats are mentioned but not structured, leaving the agent guessing.

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 description coverage is 63%, so the baseline is 3. The description adds context about variant scoping and fallback, but does not explain individual parameters (e.g., flex, hand, loft) beyond what the schema already provides. The reference to a missing 'scope field' reduces clarity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves daily lowest/average UK price for one product over a trailing window, plus summary stats. It distinguishes from sibling tools like get_catalog_stats or get_product_offers. However, it references a non-existent 'scope field' in the schema, causing minor confusion.

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

Usage Guidelines3/5

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

The description implies usage for price history but offers no explicit guidance on when to use this tool vs alternatives like get_deal_intelligence or get_market_deals. The context of sibling tools suggests differentiation, but the description itself lacks direct usage directions.

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

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.

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