Tru-Stock AI

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

Annotations already provide readOnlyHint=true and destructiveHint=false, covering safety expectations. The description adds no further behavioral context such as side effects, authentication requirements, or rate limits. It merely states the calculation and includes a promotional link, which does not contribute to 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.

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

The description is compact, with the first sentence front-loading the purpose and scope, and the second sentence providing a marketing link. The extra link is non-essential but not harmful. The content is efficient and easy to scan, earning a high score for structure.

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 straightforward calculation tool with no output schema, the description covers the core purpose and output type (total and percentage). However, it does not specify the exact response structure, any defaults, or edge cases. Given the annotations already handle safety, this level of detail is adequate but not rich.

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?

The input schema has 100% description coverage, so each parameter is already self-documenting. The description lists the cost components (warehousing, insurance, shrinkage, depreciation, opportunity cost), which map directly to parameters but do not add new semantic meaning beyond what the schema provides. This aligns with the baseline of 3 for high schema coverage.

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 clearly states the tool calculates the true annual cost of holding inventory, enumerates the cost components (warehousing, insurance, shrinkage, depreciation, opportunity cost), and specifies the output (total and percentage). This distinctly separates it from sibling tools like calculate_inventory_turnover or calculate_reorder_point, which address different inventory metrics.

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 implies the tool is for carrying cost calculations but does not explicitly state when to use it versus alternatives. There is no mention of exclusions, prerequisites, or which sibling tool to choose instead. An agent must infer the appropriate usage from the tool name and the calculation's purpose, which is only 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?

The description discloses what the tool returns (EOQ, orders per year, total annual cost) and states the optimization objective cancelling ordering and holding cost. It doesn't mention edge cases or assumptions, but annotations mark it read-only/non-destructive, so no safety contradiction.

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 short and front-loaded, with the core purpose in the first phrase and output details immediately after. The trailing marketing line ('Free tool by Tru-Stock AI') is unnecessary but not harmful.

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?

The description names the outputs and objective, which is enough for straightforward selection, but it omits assumptions (e.g., constant demand, how optional holding-cost parameters interact) and the schema's defaults are not reflected in prose. For a five-parameter calculator with no output schema, the absence of these details leaves some ambiguity.

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?

The input schema already describes all parameters (annualDemand, orderCost, unitCost, holdingCostPct, holdingCostPerUnit). The description adds only conceptual context about ordering and holding costs but doesn't provide additional parameter-level guidance or explain parameter interactions/defaults.

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 action ('Calculate the Economic Order Quantity') and defines the EOQ as the order size minimizing combined ordering and holding cost, which clearly distinguishes it from sibling inventory calculations. It also lists the computed outputs, leaving no ambiguity about what the tool does.

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?

No explicit when-to-use or when-not-to-use guidance is provided, and no alternative tools are mentioned. The intended use is implied by the EOQ definition, but the description does not help an agent choose between this and sibling calculators beyond the formula name.

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?

Annotations already signal readOnlyHint=true and destructiveHint=false, so the safe-read behavior is covered. The description adds the output metrics and input basis but does not reveal formula assumptions, precision, or response shape; that is acceptable given the annotations, so this is average rather than poor.

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 functional sentence is compact and front-loaded. The second sentence is promotional ('Free tool by Tru-Stock AI — URL') and adds no call-time value, warranting a small deduction.

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 simple calculator with read-only annotations and complete schema descriptions, the description names the key outputs and inputs. It does not spell out formulas or output units, but an agent has enough to select and call the tool correctly, with the optional revenue parameter documented in the 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% and the schema already defines cogs, annual revenue, and average inventory value. The description adds little beyond naming the same inputs, 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?

The description names a specific action ('Calculate') with concrete outputs ('inventory turnover ratio, days sales of inventory (DSI) and GMROI') and identifies the required inputs ('COGS and average inventory value'). This clearly distinguishes it from sibling tools like calculate_carrying_cost or calculate_reorder_point, which target different metrics.

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 first sentence gives clear conditions for use: compute turnover, DSI, or GMROI from COGS and average inventory. It does not explicitly state when not to use it or point to an alternative sibling, but the metric-specific wording makes the intended context obvious.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description is fully consistent with that safe, pure-calculation profile. It adds concrete behavioral context by explaining that lead time is aggregated from stages and demand is derived from it, which makes the read-only computation tangible.

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 functional sentence is front-loaded and economical. The second sentence about being a free tool and the link is not helpful for tool invocation, so it is minor waste, but the overall description remains short and readable.

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 six optional parameters and no output schema, the description gives a good high-level model but leaves gaps: it does not explain how varianceDays fits into the lead-time total, whether avgDailyDemand is required for the demand output, or what the response shape will be. It is adequate for a simple calculator but not fully complete.

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 baseline is 3. The description adds some interpretive value by naming processing, production, shipping, and receiving as lead-time stages and linking 'demand' to the calculation, but it does not mention varianceDays or clarify optionality beyond the schema.

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 uses a specific verb and resource: it 'add[s] up total vendor lead time' from named stages and 'work[s] out the demand' to cover. This clearly identifies the tool's function and distinguishes it from the sibling inventory calculators, which target different metrics.

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 implies when the tool is appropriate—whenever an agent needs vendor lead time or demand-during-lead-time—but it gives no explicit guidance about when not to use it or which sibling alternatives to prefer. Context is clear enough for a simple calculator, but no exclusions or alternative routing are provided.

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?

Annotations already mark the tool as read-only and non-destructive. The description adds meaningful behavioral context by naming the King formula and specifying that it returns the reorder point, safety stock, and demand during lead time, going beyond what annotations provide.

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 core description is front-loaded and efficient, with a clear first sentence and a concise list of outputs. The promotional URL and 'Free tool by' sentence are not strictly necessary for tool invocation, preventing a perfect score.

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?

All required and optional parameters are documented in the schema, and the description covers the return values despite the absence of an output schema. The main gap is the lack of guidance distinguishing this tool from the safety stock sibling, but the tool remains invocable and understandable.

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?

Input schema coverage is 100%, so the schema already documents every parameter. The description adds the formula context but does not explain how individual parameters like demandStdDev or serviceLevel interact, which the schema also does not fully cover.

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 clearly states the tool calculates the reorder point for an inventory item and lists the returned values. It does not explicitly distinguish itself from the closely related calculate_safety_stock sibling, so it misses the top score.

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?

No explicit guidance is given for when to use this tool versus siblings like calculate_safety_stock. The phrase 'when to place a new order' implies the intended use case, but there are no when-not-to-use instructions or alternative routing.

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?

Annotations already carry readOnlyHint=true and destructiveHint=false, so the safety profile is fully covered. The description adds value beyond that by revealing the computation method (King formula), the return content ('returns units and the capital tied up'), and the commercial origin. No behavioral side effects are hidden; the only minor gap is not stating the default service level behavior when the parameter is omitted (though the schema covers that).

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-load the purpose and method, then the return content. The only filler is the promotional URL and branding ('Free tool by Tru-Stock AI'), which is harmless but adds no decision-making value for the agent. The core information is compact and well ordered.

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 pure-compute tool with six straightforward parameters, high schema coverage, and annotations already declaring it non-destructive, the description is complete. It names the formula, states what the call returns (units + capital), and implies the input variables. Since there is no output schema, the simple 'Returns...' statement fulfills the disclosure job; nothing an agent needs to choose and call the tool correctly is prematurely minimal.

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 all six parameters are fully described in structured form — the baseline for this dimension is 3. The description adds context about the formula (e.g., that the King formula incorporates demand/lead-time std devs) but does not explain parameter interactions such as how the tool behaves when standard deviation fields are omitted. The schema does the heavy lifting here, so a 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 states a specific verb + resource ('Calculate safety stock'), the purpose ('protect against demand and lead-time variability'), the exact method ('King formula'), and what it returns ('units and capital tied up'). This clearly differentiates it from siblings like calculate_reorder_point (reorder point, not safety stock) and estimate_trapped_capital (idle stock value, not safety stock calculation).

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 phrase 'to protect against demand and lead-time variability' gives clear usage context, implicitly signaling when an agent should reach for this tool versus a plain EOQ or turnover calculator. However, it does not explicitly name a sibling alternative nor state when not to use it, leaving a bit of inference to the agent.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond the annotations: it explicitly warns that the tool returns a RANGE rather than a single number when inputs are left at defaults, and explains why (defaults are assumptions). It also discloses that it is a free tool by Tru-Stock AI, which is useful context for an agent deciding whether to invoke it.

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 compact and front-loaded: the core purpose and the key behavioral caveat (range vs single number) appear in the first two sentences. The final sentence adds promotional/attribution context that is not strictly necessary for tool invocation, which keeps it from a 5, but it is short and not bloated.

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 read-only estimation tool with 100% schema coverage and no output schema, the description covers the essential invocation context: what it computes, that it uses user-supplied figures, and the range behavior under defaults. It doesn't describe the output format in detail, but with no output schema and a simple calculator, the description is adequate. The only minor gap is not explicitly stating that the tool is non-destructive, but annotations already cover that.

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 all six parameters with descriptions and defaults. The description adds the high-level meaning that the parameters feed into a trapped-capital estimate, but it doesn't add per-parameter semantics beyond what the schema provides. 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 opens with a specific verb ('Size') and a concrete resource ('the cash trapped in inventory'), then enumerates the three loss components (dead stock, stockout losses, carrying cost). It clearly distinguishes itself from the sibling calculators by framing the output as a sizing/estimation of trapped cash rather than a single metric like turnover or EOQ.

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 states that it works 'from your own figures' and explains that default inputs are assumptions, which implies the user should supply their own data for accurate results. It doesn't explicitly name sibling alternatives or say when not to use this tool, but the context signals and the tool's unique scope make the usage context reasonably clear.

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