TechPartPrices

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

With no annotations provided, the description carries the behavioral burden well: it discloses sort order, pence-denominated unit prices, and the row-level fields including 90-day low, sample count, and at-low/near-low verdict. It does not explicitly state read-only behavior, but the 'get' naming and ranking description make that clear enough for this read-oriented tool.

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 three tight sentences, front-loaded with the core ranking behavior and followed by pricing units and row contents. Every sentence adds useful information without repetition or filler.

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 three-parameter ranking tool with no output schema and no annotations, the description covers the essential behavior, sorting, units, and returned row fields. It omits some detail about category filtering and how 'near-low' is determined, but those are secondary given the rich input schema.

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%, so the schema already documents unit, limit, and category. The description adds useful rationale for why unit is required because the two scales cannot be compared, but it does not add further semantics for limit or category beyond what the schema already provides.

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 states a specific verb and resource: rank tracked storage or memory by price per terabyte/gigabyte, cheapest first. It is clear what the tool does, but it does not explicitly distinguish itself from siblings such as get_price_verdict or get_price_index.

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 explains what the tool returns and why the unit parameter is required, but it gives no guidance on when to use this tool instead of alternatives like get_price_history or get_price_verdict. There are no stated exclusions or decision criteria for an agent choosing among the sibling tools.

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

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

With no annotations, the description carries the full burden. It does disclose the shape of the returned data (min, max, average, direction of travel), which is genuinely useful. It does not state read-only safety explicitly, what happens when a product has no recorded price history, or how sparse/partial windows are handled.

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?

Two sentences, front-loaded with what the tool returns before the usage framing. Little waste, though the quoted user questions are slightly conversational padding for an agent-facing definition.

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 two-parameter read tool with full schema coverage and no output schema, the description covers what the tool does and roughly what comes back. The main remaining gap is behavior on empty or insufficient history, which matters for a history-reading tool.

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 coverage is 100%, so both the id and the days enum (7/30/60/90, default 30) are already fully documented. The description's 'trailing window' phrasing echoes the days parameter but adds no syntax or edge-case detail, so the baseline 3 applies.

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?

States the resource (recorded price points for a product) and the trailing-window scope, plus the aggregate fields returned. It is clear what the tool produces, but it never names or distinguishes itself from siblings like get_price_verdict or get_price_index, which sound like they cover adjacent questions.

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 second sentence gives implied usage context ('is now a good time to buy', 'has this got cheaper'), which is more than nothing. However, there are no exclusions, and the stated use case plausibly overlaps with the sibling get_price_verdict, so the agent gets no routing rule for choosing between them.

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

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

No annotations exist, so the description carries the burden and does reasonably well: it discloses the index base (100 at series start), that the result is an aggregate not a product, the default scope ('all', whole catalogue), and how to judge freshness via lastComputedAt. It doesn't explicitly state read-only/no-side-effects, but the framing strongly implies a pure read.

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?

Three short sentences, all load-bearing: purpose first, usage/alternative second, freshness hint third. No filler or repetition of the title.

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 single-optional-param read tool with no output schema, the description covers what the data means (index, base 100, category granularity) and freshness. It could say slightly more about the response shape (e.g. the series/points structure) since no output schema exists, but nothing critical 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 coverage is 100%, so the parameter is already fully documented, including the default and availableCategories. The description only mildly reinforces the category-level semantics ('describes a category, not an individual product') and adds no syntax or format detail beyond the schema. Baseline 3 applies.

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?

States a specific verb and resource (month-by-month price movement for a category of UK components) plus the index convention (base 100 at series start). It explicitly distinguishes itself from get_price_verdict as category-level vs single-product, but never mentions the sibling get_price_history, leaving a plausible overlap unresolved.

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?

Gives a concrete use case ('answer whether prices in a category are rising or falling') and an explicit alternative for the individual-product case ('for one product use get_price_verdict'). No exclusion against get_price_history, which is the one remaining routing ambiguity.

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

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

With no annotations, the description carries the full burden and does well: it discloses the return contents, the minimum-sample threshold, the verdictAvailable flag, and an explicit instruction not to present numbers as a judgement below that threshold. It omits any statement about read-only safety, unknown-ID behavior, or caching, which keeps it short of a 5.

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?

Four dense sentences, front-loaded with the core question and its distinguishing scope, followed by return contents, the caveat, and the ID source. No filler and no repetition of schema fields.

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 no output schema and no annotations, the description still covers what the agent needs: the judgement semantics, the returned fields including buyUrl, the low-data guardrail, and where IDs come from. Nothing essential for correct invocation 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 coverage is 100%, so both id and the days enum (30/60/90, default 90) are already documented in the schema. The description only alludes to the window via 'window low, high and mean' and the ten-price threshold; it adds no format or selection guidance beyond the schema, so baseline 3 applies.

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?

States a specific verb and resource — judge whether a tracked product is cheap right now — and immediately scopes it as 'by its own recorded history rather than against other products', which separates it from get_best_value and get_price_index. It also enumerates what the answer contains (window low/high/mean, distance above low, verdict, buyUrl).

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?

Gives a concrete applicability rule (fewer than ten recorded prices means verdictAvailable is false and no verdict should be presented) and routes the agent to search_products for the required ID. It does not explicitly say when to prefer get_price_history over this tool, so the sibling boundary on the history side is left implicit.

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

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

With no annotations, the description carries the burden, and it does disclose the shape of the result and the units of the price ('in pence'), which prevents a plausible unit error. The buyUrl caveat is a genuine behavioral constraint that no structured field conveys. It does not state failure behavior for an unknown ID or whether results are cached/live, so it falls short of full disclosure.

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?

Three sentences, each earning its place: what is fetched, the buyUrl rule, and the ID source. The most error-prone caution (buyUrl) is placed before the input-provenance note, and there is no filler.

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?

No output schema exists, so the description compensates by naming the returned fields, their units, and the one field (buyUrl) whose misuse is likely. For a single-parameter read tool with no annotations, nothing an agent needs in order to call it correctly 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 coverage is 100% and there is a single documented parameter, so the baseline is 3. The description adds provenance ('IDs come from search_products') and the constraint that the ID is numeric, which is mildly useful, but it does not resolve the main ambiguity — whether the ID is the same identifier returned by search_products or an ASIN-like value.

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?

Specific verb and resource ('Fetch one tracked product by its numeric ID'), plus explicit enumeration of the returned fields (name, brand, price in pence, stock state, rating). It is clearly distinguished from search_products, which is named as the source of IDs rather than a substitute.

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?

'IDs come from search_products' tells the agent where input originates and implies this tool is the follow-up step to a search. The buyUrl sentence gives explicit do/don't behavior ('never rebuild an Amazon link from the ASIN'), which is real usage guidance rather than a restatement. It stops short of stating when this tool should not be used (e.g., bulk retrieval, price history).

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

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

With no annotations, the description carries the full disclosure burden. It adds one genuinely useful behavioral fact — results come back most recent first — but says nothing about whether the list is read-only (only implied by 'index'), how deep pagination goes, or what happens when page exceeds the article count.

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?

One sentence, front-loaded with the resource and scope, with ordering appended. Nothing is wasted and nothing needs trimming.

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 one-optional-parameter listing tool with no output schema, the description covers the essentials: what is listed, what it is about, and the sort order. The remaining gap — what fields an article entry contains and how pagination terminates — is modest given the tool's simplicity.

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% and the single optional page parameter is already documented with its default in the schema. The description adds no paging semantics beyond that, so the baseline of 3 applies.

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?

States a specific verb (list) and resource (published articles) with the subject scope spelled out: UK component pricing and buying advice. It also declares the sort order, which helps an agent predict output. It does not explicitly contrast itself with siblings, but the siblings (get_product, search_products, get_price_*) are product/price tools, so the article-index role is unambiguous by omission.

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?

Usage is only implied: an agent can infer this is the browse/discovery entry point for editorial content versus the data-lookup siblings. There is no explicit 'use this when...' statement, no exclusions, and no mention of when to prefer search_products or the price tools instead.

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

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

With no annotations at all, the description carries the load well: it discloses that prices are in pence, that results are capped at 10, what fields come back (price, stock, url, buyUrl), and a concrete behavioural rule to never rebuild an Amazon link from the ASIN. It omits any mention of auth needs, data freshness/caching, or rate limits, which would round out a zero-annotation tool.

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?

Three tightly packed sentences: scope and catalogue first, then return shape and unit convention, then the usage/buyUrl rule. Nothing is redundant and the read-critical facts are front-loaded.

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?

There is no output schema, and the description compensates by naming the returned fields and their currency units, which is exactly what an agent needs to consume results. With all 7 parameters optional and fully documented in the schema, nothing needed for a correct call 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%, so every parameter already carries its own description and enum meanings. The description's only added semantics is the price-in-pence convention, which the schema already states for minPrice/maxPrice, so a baseline 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 gives a concrete verb+resource ('Search tracked Amazon UK listings') plus the exact category scope it covers (CPUs, GPUs, RAM, motherboards, storage). It stops short of naming a sibling such as get_product or get_best_value, so the distinction from neighbouring tools is only implied through query examples.

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?

It explicitly frames the use case as answering 'what does X cost' and 'what is the cheapest Y', which gives an agent solid context for picking it. It never states when NOT to use it or which sibling to prefer for a single known product or a price verdict, so no exclusions are offered.

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