NestingCalc Calculators

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

With no annotations, the description carries the full behavioral burden. It does disclose key behavior: equal spacing, a maximum-gap enforcement, and the returned items. However, it does not explain how the tool behaves if equal spacing cannot satisfy the maximum gap, nor what 'positions' means relative to the run, leaving some ambiguity.

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 a single concise sentence that front-loads the purpose, lists the outputs, and then states the key constraint. Every clause adds meaningful information and there is no redundancy with the schema.

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 three-parameter calculator, the description and schema provide the essential inputs and outputs. However, the output format for positions, handling of edge cases when the max gap cannot be met, and unit assumptions are not fully clarified. These gaps make the description slightly incomplete for fully autonomous invocation without probing.

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 schema already describes run_mm and max_gap_mm, and the description reinforces max_gap with the sphere-code example. It also helps contextualize baluster_width_mm as the width of the baluster/spindle, but it does not explain parameter relationships or output representation. With 67% schema coverage, this is adequate but not fully compensating.

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 the specific operation: equal baluster/spindle spacing for a run, and enumerates the outputs: number of balusters, exact gap, and positions. This is not a tautology and clearly differentiates the tool from sibling calculators like linear_cutting_optimizer or board_feet_calculator.

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 'for a run' plus the 4-inch/100 mm sphere code rule gives a clear scenario: when an agent needs to lay out balusters or spindles with a maximum gap constraint. There are no explicit exclusions or alternative tool references, so it does not earn a 5, but the usage context is clear.

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 provided, the description carries the full burden of behavioral disclosure. It mentions the formula, support for multiple boards, and optional pricing, but does not disclose output format, whether results are aggregated or per-board, rounding behavior, or how unit conversions are handled. This leaves meaningful gaps in predicting tool behavior.

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 two sentences with no filler. It front-loads the core purpose and formula in the first sentence, then adds the key modifiers (multiple boards, optional price, price units) in the second. Every word earns its place.

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 tool has 4 parameters, no output schema, and no annotations, so the description must cover enough for correct invocation. It explains the calculation and pricing options, but does not specify output structure (e.g., totals vs. line items, cost if price omitted). Given the simplicity of the domain and thorough schema coverage, it is acceptable but has clear gaps for an agent expecting detailed return semantics.

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 documents all parameters with 100% coverage, but the description adds meaningful context beyond the schema by explaining the board-foot formula and how price relates to price_unit (per bf or MBF). It clarifies the purpose of the optional price and the multi-board aggregation, adding value over the raw 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 clearly states the tool calculates board feet and lumber cost, provides the formula (thickness x width x length / 144 in inches), and contrasts with sibling tools by focusing on lumber pricing. This is specific and distinguishable from related calculators like lumber_weight_calculator or log_volume.

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 use for board foot and lumber cost calculations, but it does not explicitly state when to use this tool versus alternatives or provide conditions/exclusions. There is no mention of alternatives or when-not-to-use, leaving the agent to infer applicability.

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 behavioral disclosure burden. It transparently explains the calculation logic for buy quantity, cost outputs, and selling price, and even provides verified waste presets. It does not mention side effects, but as a calculation tool this is sufficient.

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 a single dense sentence with no filler, front-loading the core purpose and formulas. It is somewhat long and packed, but every clause earns its place by conveying essential costing behavior.

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?

Despite having no output schema and no annotations, the description covers the main outputs (line/total costs, per-product cost, selling price) and the formulas. It is slightly incomplete because it omits the role of labor_per_product and does not explicitly state default behavior, though schema covers defaults.

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 most parameters, so the baseline is 3. The description adds meaningful semantic value by defining the buy-quantity formula, waste presets, and margin-based price formula. However, it does not explain how labor_per_product factors into the cost roll-up.

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 ('cost roll-up') and names the exact resource ('bill-of-materials ... for furniture'). It includes concrete formulas, which makes the tool's purpose unmistakable and clearly distinguishes it from the sibling measurement/estimation calculators.

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 context is implied through 'for furniture' and the BOM roll-up wording, but there is no explicit when-to-use guidance or comparison with alternatives such as manufacturing_yield or rough_sizing. No exclusions are stated.

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 are provided, so the description carries the burden. It discloses that the tool computes volumes, weight, density, and a verdict, and it specifies the density threshold. However, it does not state that the operation is purely read-only/calculative, how omitted weight_kg is handled, or what happens with invalid inputs beyond schema constraints.

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 a single dense sentence that front-loads the core outputs and threshold. It has no fluff, though the opening phrase 'Packing CBM for cartons' is grammatically awkward and could have been phrased more clearly as a calculation action.

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?

Given one parameter, no annotations, and no output schema, the description names all computed outputs and the decision threshold, which is mostly sufficient for a simple calculator. Gaps remain around the exact meaning of the cube-out/weigh-out verdict and the handling of optional weight_kg, but the tool is simple enough that these are not fatal.

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 baseline is 3. The description adds context that inputs are per carton dimensions and that outputs are aggregate volume and density, which slightly reinforces the meaning of the parameters, but it does not provide syntax or conversion details beyond what the schema already conveys.

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 identifies a carton CBM packing calculation and enumerates the outputs: per-line/lot volume, total weight, lot density, and a cube-out/weigh-out verdict. It distinguishes itself from sibling calculators like container_loading by focusing on carton lines, though it lacks an explicit verb such as 'calculate'.

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?

There is no explicit guidance on when to use this tool versus alternatives. The mention of 'same presets as container loading' hints at a relationship with container_loading, but it does not tell an agent when to choose cbm_shipment over container_loading or other volume/density calculators.

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 are provided, so the description carries the full burden of behavioral disclosure. It states it computes values but does not explicitly say what it returns (RPM and feed rate), nor does it mention any side effects, error handling, or assumptions. The description is too sparse to inform the agent about the tool's behavior beyond a basic calculation.

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 a single sentence that is concise and front-loaded with the primary purpose. It contains no redundant words and conveys the essential information efficiently.

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?

Given the tool has no output schema and no annotations, the description should state what the tool returns. It mentions 'spindle RPM and feed rate' but does not explicitly say these are returned, nor does it describe the output format (e.g., values in which units). It also omits any details about how the calculation differs between router and mill, which could be relevant. The description is insufficient for an agent to fully understand the tool's capabilities and expected outputs.

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 50%, with descriptions for tool_diameter, chip_load, and cutting_speed. The description compensates by mentioning all key parameters (tool diameter, flute count, chip load, cutting speed) and clarifies the unit system (metric/imperial) and machine types (router/mill), which are not described in the schema. This adds context for the undocumented parameters, though it does not explain the exact formulas or units of output.

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 computes CNC spindle RPM and feed rate from specific inputs. It is specific and mentions the resource (CNC machining) and the parameters used. However, it does not explicitly differentiate itself from sibling tools like rpm_calculator or cutting_speed_calculator, which could overlap in purpose.

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 provides no guidance on when to use this tool versus alternatives. It mentions it works for routers and mills and supports metric/imperial, but does not state conditions for choosing it over similar calculators. There is no mention of exclusions or when not to use it.

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 provided, the description carries the burden of behavioral disclosure and does so well: it explains that presets are editable, that both volume and weight limits are considered, and that it returns the binding limit and containers needed. It doesn't discuss rounding/validation edge cases, but for a calculator this is adequate.

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, front-loaded with the central question, and every sentence adds value: the presets, editability, and return values are all essential. No filler or redundant restatement of the schema.

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?

Given the rich schema and detailed output description, the definition is complete for practical use. It does not explicitly say that carton dimensions or volume must accompany kg_per_piece, but the schema supplies those fields and their descriptions, so an agent can infer the required inputs.

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 high at 89%, but the description adds meaningful context beyond the schema: it explains that the container presets are editable and that planned_qty drives the 'containers needed' output. This goes beyond simply restating field names.

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 outcome: determining how many uniform cartons fit in a shipping container before cube-out or weigh-out. It clearly names the resource and computation, though it does not explicitly distinguish itself from closely related siblings like cbm_shipment or stowage_factor.

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 use case is clear: container loading/cube-out vs weigh-out planning for uniform cartons. It gives enough context for an agent to recognize when this tool applies, though it does not name alternatives or state exclusions.

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 provided, the description carries the full burden of behavioral disclosure. It transparently states that the tool always returns the speed converted to the other unit system and that a pre-filled web_url is included. This exceeds what is visible in the schema and helps the agent anticipate output behavior.

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 concise and front-loaded: it states the core calculation, gives formulas, notes the conversion behavior, and mentions the web_url in three efficient sentences. No filler or redundant restatement of parameter names.

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 explains the calculation and output behavior but does not fully specify the response structure. Since no output schema is present, the agent must infer the exact fields returned beyond the web_url and the converted speed, leaving some ambiguity for a tool with no output 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?

Although schema coverage is 100%, the description adds semantic value by defining the mathematical relationship between diameter (D) and spindle speed (n), clarifying how the parameters interact and how unit selection affects the formula. This goes beyond the schema's simple property descriptions.

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 relationship: it calculates cutting speed from spindle speed using explicit formulas for both metric and imperial. This clearly distinguishes it from sibling tools like rpm_calculator, which would perform the inverse 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 formula and 'Cutting speed from spindle speed' imply the use case: use when you know spindle speed and diameter. However, it does not explicitly state when not to use this tool or mention the sibling rpm_calculator as the alternative for the inverse calculation, so guidance is implied rather than explicit.

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 provided, the description carries the full burden of behavioral disclosure. It does this well by stating exactly what the tool returns: stitch stats before/after, a density safety verdict with an explicit safe range (+/-15%), and the resized file as base64. It goes beyond a simple action statement, making the tool's observable behavior predictable.

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 a single, tightly packed sentence that leads with the primary action and immediately covers input format, scaling method, and the full relevant output set. No words are wasted and every clause carries essential information.

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?

Given there is no output schema and no annotations, the description is remarkably complete: it names the accepted source formats, the operation to perform, and all three pieces of returned information (stats, safety verdict, base64 file). An agent has everything needed to invoke the tool correctly and interpret its response.

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 provides complete descriptions for both parameters (file_base64 and scale_percent), including the example '80 for -20%'. The description's prose adds no new semantic detail about parameter formats, ranges, or constraints beyond what is already in the schema, so the baseline of 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, actionable verb ('Resize') and a clear resource ('an embroidery design file (PES or DST)') followed by the operation's key dimension (scale percentage). It is immediately distinguishable from the sibling tools, which are all generic calculators, and leaves no ambiguity about the tool's domain or function.

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 implicitly conveys when to use it: whenever a PES or DST embroidery file needs its size changed. It does not explicitly list exclusions or alternative tools, but the context is clear enough that an agent can select it without confusion, especially since none of the sibling tools relate to embroidery resizing.

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 behavioral burden. It usefully discloses the unit definitions and optional species/moisture-based weight estimate, but it does not explain what the tool returns, how a target unit is selected, or why 'solid m3' appears in the description but not in the schema's 'from' enum.

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?

A single sentence with no filler: it front-loads the action, captures the unit scope, and mentions the optional weight estimate. Every clause contributes information.

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 no annotations, so the description alone must clarify output behavior. It never states what a successful result looks like, how the output unit is chosen, or that the tool returns all conversions. The mismatch between 'solid m3' in the description and its absence from the input enum further weakens completeness.

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 83%, so parameters are already well-documented. The description adds context about the unit families and weight estimate, but largely restates what the schema already says without adding deeper parameter meaning.

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 verb ('Convert') and resource ('firewood volume'), lists the supported units, and adds the optional weight-estimate capability. This clearly distinguishes the tool from siblings such as timber_unit_converter or board_feet_calculator.

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 firewood-specific domain and listed units imply when the tool should be used, and the tool is clearly set apart by content. However, it never explicitly says when not to use it, nor does it name alternatives like timber_unit_converter for generic conversions.

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 of explaining behavior. It discloses that the calculation includes 'offcut reuse between rows (best-fit)', expansion gap, layout waste presets, and direction, which are meaningful algorithmic behaviors beyond the bare schema.

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 dense sentence with zero filler. The domain is front-loaded, followed by outputs and the key behavioral features, making it easy to scan while retaining high information density.

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 12-parameter calculator with no output schema and no annotations, the description conveys the essential return values (planks, boxes, cost) and core modeling choices. It is compact but sufficient for an agent to select the tool; minor gaps remain around exact output structure and cost input priority.

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 only 25%, and the description helps by grouping features (planks, boxes, cost, offcut reuse, expansion gap, direction) that map to otherwise undocumented parameters. However, it does not clarify ambiguous relationships such as price_per_m2 versus price_per_box or how waste_percent interacts with layout presets, so it only partially compensates.

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 domain and resource ('Laminate/vinyl plank takeoff') and enumerates concrete outputs ('planks, boxes and cost') plus distinctive features such as offcut reuse, expansion gap, waste presetsees. This clearly separates it from siblings like tile_calculator or linear_cutting_optimizer.

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 'Laminate/vinyl plank takeoff' establishes a clear context for when the tool applies, and the offcut/layout focus distinguishes it from general material calculators. It does not explicitly name alternatives or hard exclusions, so it earns a 4 rather than 5.

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 behavioral burden. It discloses the weight formula (2.5 kg/m2 per mm of thickness), the optional waste and edge-work additions, cost outputs, and the web_url with a prefilled quote. It does not discuss edge cases or exact response formatting, but it is transparent about the tool's main computational behavior.

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 a single front-loaded sentence that leads with the tool's core purpose, then uses a colon-delimited list to enumerate calculations and outputs without filler. Every clause earns its place.

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 7-parameter calculator with no output schema or annotations, the description covers the essential invocation context: inputs, calculation formulas, optional additions, total/per-piece results, and the quoted web URL. It could add output field names or formatting details, but the description is sufficient for correct tool selection and invocation.

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 real semantic value beyond the schema by explaining how thickness_mm relates to weight and how waste_pct is applied on top of net area, which helps an agent understand the calculation model.

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: 'Cut-to-size glass cost and weight estimate.' It lists exact outputs and distinguishes this tool from sibling calculators by making glass the unmistakable subject.

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 gives clear context for when to use the tool: whenever a cut-to-size glass cost and weight estimate is needed. It does not explicitly name alternatives or exclusions, but the material-specific focus makes the appropriate use unmistakable among sibling calculators.

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 are provided, so the description carries the full burden. It discloses the calculation logic and the two special modes (edge-glued panels, face lamination) but does not state whether the tool is read-only or mention any side effects. As a calculator, it is implicitly non-destructive, but the description does not explicitly confirm this, leaving a moderate gap.

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 two sentences with no wasted words. The first sentence front-loads the core formula and key presets; the second lists the two additional computation modes. It is efficiently structured and immediately informative.

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?

Given the tool's complexity (8 parameters, 2 nested objects, no output schema), the description covers the main calculation and alternatives but omits any mention of what the tool returns (e.g., mass, volume, cost) or how the output is structured. Since density_gcm3 and price_per_kg suggest optional cost/volume outputs, this is a notable gap for an agent deciding whether this tool fits a task.

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 meaningful context by explaining the overall formula and providing preset spread values (e.g., PVAc D3 175) that clarify the spread_gsm parameter. It also explains how the nested objects (edge_panel, face_lam) serve as alternative ways to compute bond area, which goes beyond the individual parameter descriptions.

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 it computes wood adhesive quantity using a specific formula (bond area x spread x faces x waste allowance) and also handles edge-glued panels and face lamination. This is a specific verb-resource pairing that distinguishes it from the many other woodworking calculators, none of which target glue consumption.

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 provides clear context for when to use this tool (any wood adhesive quantity calculation) and gives presets for common adhesives, but does not explicitly mention when not to use it or name alternative tools. Since no sibling tool is glue-specific, the context is sufficient.

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 are provided, so the description carries the burden. It discloses the calculation method, the source, and the return values (oven-dry mass, water mass, tonnes). However, it does not disclose edge cases like what happens if both species and basic_density_kg_m3 are provided, or if mc_pct is omitted (defaults to species' freshly-felled MC). The description adds some behavioral context but not comprehensive detail.

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 two sentences, front-loaded with the formula and scope, and includes the source and return values. It is efficient and structured, though the list of species could be seen as slightly verbose. Overall, it earns its place.

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 calculation tool with 4 parameters, 100% schema coverage, and no output schema, the description is fairly complete. It explains the formula, the source, the presets, and the return values. It could be more complete by explaining the precedence between species and basic_density_kg_m3, and the default MC behavior, but these are minor gaps given the schema already documents defaults.

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 four parameters. The description adds context about the formula and the species presets, but does not add much beyond the schema. Baseline 3 is correct because the schema does the heavy lifting.

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 ('Weight of a roundwood/log batch'), the resource (roundwood/log batch), and the formula (W = volume x basic density x (1 + MC/100)). It also names the source (USDA Wood Handbook 2021 Ch.4) and distinguishes itself from siblings like lumber_weight_calculator and log_volume by focusing on roundwood/log batch weight at any moisture content. This is a clear, specific purpose that an agent can act on.

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 to use this tool: when you need the weight of a roundwood/log batch at a given moisture content, with presets for species or custom density. It does not explicitly state when not to use it or name alternatives (e.g., lumber_weight_calculator for sawn lumber), but the formula and 'roundwood/log batch' scope provide clear context. A 4 is appropriate because the usage context is clear, though exclusions are not explicit.

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 are provided, so the description must fully disclose behavior. It mentions optional quantity and value calculations, but does not disclose unit handling defaults beyond the schema's 'unit' parameter, nor any limitations (e.g., assumes circular cross-section, no bark deduction). The description adds some value but is not thorough for a calculation tool with no annotations.

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 a single sentence that is informative and front-loaded with the calculation method. It covers multiple outputs and options efficiently, though it could be slightly more structured with a list of outputs for readability.

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, the description must explain return values, which it does (lists Hoppus volume, true volume, tons, board-feet approximation). However, it does not mention what 'true volume' means or how the board-feet approximation is derived, and it omits details about unit conversion defaults. For a domain-specific tool, this is adequate 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 coverage is 80%, and the description adds meaning by explaining that 'circumference' is at mid-length and that volume is computed via (C/4)^2 x L. It also clarifies the output units (Hoppus ft3/m3) and optional quantity/value, which helps the agent understand parameter relationships 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 states a specific verb ('Returns') and resource ('Hoppus volume of round logs') and includes the formula, distinguishing it from siblings like 'log_volume' and 'jas_log_scale' by naming the exact method. It clearly identifies the tool's unique calculation approach.

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 to use this tool (for Hoppus/quarter-girth calculations) but does not explicitly state when not to use it or mention alternatives. Given the sibling list includes other log volume calculators, explicit routing would be beneficial.

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 reveals the rounding rule (floor to 1 cm below 14 cm, even 2 cm at 14 cm and above), oval-log averaging, the optional +2 cm ovality adjustment, the over-6 m length allowance, and the m3 output. It does not detail how qty affects totals or how diameter_long_cm is handled, but it is substantially transparent.

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 dense but compact, leading with the standard name and then the key behavioral specifics. It contains no filler and every phrase adds information, though a single long sentence with semicolons is slightly harder to parse than a short bulleted 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 tool with no output schema, it states the output format (per log and totals in m3) and enough JAS-specific behavior to make a correct call plausible. It does not quantify the exact length allowance or the full volume formula, but the named official standard plus the explicit rounding rules make it reasonably 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 already documents 80% of the parameters, so the baseline is 3. The description adds operational meaning by tying diameter inputs to the rounding and ovality rules and by connecting length_m to the over-6 m allowance. qty remains only implicit in 'totals,' but the schema default covers that gap.

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 the exact standard (JAS), the computation (diameter rounding, oval averaging, +2 cm adjustment, length allowance), and the result (volume per log and totals in m3). It clearly distinguishes this specialized JAS tool from generic siblings like log_volume and hoppus_volume.

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 usage when JAS-standard scaling is needed, but it never explicitly states when to choose this tool over alternatives such as log_volume, hoppus_volume, or green_log_weight. The 'official' framing gives context, but there are no exclusions or alternative routing conditions.

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 it does reasonably well: it reveals the calculation method (D×R×C adjusted by sticker thickness and fill factor), the typical fill-factor range, and the m³-to-board-foot conversion. It does not describe the exact return shape or edge cases, but the core behavior is transparent.

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: purpose, formula, parameter meanings, and output conversion all fit in two sentences with no redundant filler. Every phrase contributes.

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?

Given no output schema and no annotations, the description is mostly adequate—it gives the formula, variable meanings for t/s/f, and output units—but it does not define D/R/C or describe the returned data shape, leaving noticeable context gaps for an agent invoking the 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 the baseline is 3. The description adds meaning by mapping t, s, and f to board thickness, sticker thickness, and fill factor and by giving a typical fill-factor range, but it leaves D, R, C unmapped to the schema's length_m, width_m, and height_m, so it stays at baseline.

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 clear function: compute kiln charge capacity / wood volume per batch, backed by an explicit formula and output units. It is not a tautology and goes beyond the name, though it doesn't explicitly differentiate itself from sibling calculators such as board_feet_calculator.

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 intended context is implied by 'Kiln charge capacity' and the formula, so an agent can infer when to use it. However, there is no explicit statement of when to use this tool instead of the many related lumber/volume calculators, nor any exclusions or prerequisites.

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 discloses the heuristic (best-fit decreasing), implying non-optimal results, and lists expected outputs (bars used, cut lists, waste, unplaced parts) plus a web_url for visualization. It does not mention potential failure modes like parts exceeding stock length, but covers the core behavioral traits sufficiently.

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 with no redundancy. The first sentence states the purpose and heuristic, the second lists outputs and the web_url. Information is front-loaded and every word earns its place.

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?

Given the tool has 6 parameters and no output schema, the description covers the essential return values (bars used, cut lists, waste, unplaced parts) and the visual aid. It does not explain error handling or edge cases, but those are not critical for a calculator tool. The description is sufficiently complete for an agent to invoke it correctly.

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 baseline is 3. The description adds the algorithm context (best-fit decreasing) which indirectly informs how parameters like kerf and trim affect results, but it does not explicitly explain any parameter beyond what the schema already provides. It adds marginal value, consistent with the baseline.

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 optimizes 1D cutting plans for bars, pipes, and profiles, specifying the resource and the operation. It also names the heuristic (best-fit decreasing), which distinguishes it from the 2D sheet_cutting_optimizer sibling. The purpose is unambiguous and distinct.

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 usage for linear materials but does not explicitly contrast with alternatives like sheet_cutting_optimizer. It mentions the linear cutting stock problem, giving context, but lacks explicit when-to-use or when-not-to-use guidance. An agent would infer the use case from '1D' but not receive direct routing instructions.

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 it does disclose output behavior (returns both m3 and ft3), input unit conventions, and which diameters each formula requires. It doesn't mention defaults or behavior on invalid/missing diameters, but those are minor for a pure calculator with schema-provided defaults.

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 dense sentence delivers formula options, parameter implications, unit systems, and return units with no filler. The core purpose is front-loaded and every clause earns its place.

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 no-annotation, no-output-schema calculator, the description covers the key call decisions: method selection, required diameters per method, unit systems, and return values. Minor omissions are explicit defaults and exact output field names, but the schema supplies the defaults and the description supplies the output nature.

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 description maps the method enum to the relevant diameter parameters (mid only, both ends, all three) and translates the unit enum into concrete dimensions, which the schema only partially covers. It compensates well for the 67% schema description coverage, though the length parameter remains undocumented in both places.

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 resource (a log) and operation (computing cubic volume), and names the specific formulas (Huber, Smalian, Newton), which distinguishes it from sibling log-volume scales like hoppus_volume and jas_log_scale. It also specifies unit systems and return units, leaving no ambiguity about what the tool computes.

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?

There is no explicit guidance on when to choose log_volume over sibling log-scale tools such as hoppus_volume or jas_log_scale, nor any statement about when not to use it. The 'most accurate' note on Newton is internal method-selection guidance, not tool-selection guidance.

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 must carry full behavioral disclosure. It does reveal that calculations use species density tables and allow optional custom density override, which is useful. However, it omits the output unit and any underlying assumptions, such as metric inputs or how moisture affects the result beyond what the schema states.

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 entire description is one concise, front-loaded sentence with no filler. Every phrase carries meaning: 'Weight of lumber', the parameter categories, and the optional custom density.

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 7-parameter calculator with no output schema and no annotations, the description covers the core inputs but leaves gaps: the output unit is not explicitly stated, and there is no routing guidance to distinguish from board_feet_calculator or green_log_weight. It is sufficient for a straightforward calculation 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 43%, leaving most parameters undocumented. The description adds a semantic umbrella covering species, dimensions, quantity, moisture, and custom density in kg/m3, which helps interpret the unnamed numeric parameters. Yet it does not specify dimension units (though property names imply mm/m) or elaborate on moisture states beyond the schema's own description.

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: 'Weight of lumber by species, dimensions, quantity and moisture (dry/fresh)'. This is not a tautology and the scope clearly differentiates it from related siblings like board_feet_calculator (volume) and green_log_weight (logs), even without naming them explicitly.

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 provides no guidance on when to use this tool over alternatives. It does not mention sibling tools such as green_log_weight or board_feet_calculator, nor does it state any exclusions or conditions. Usage must be inferred entirely from the name and terse description.

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 of explaining behavior. It discloses the calculation pipeline and the outputs: per-stage loss and required roundwood volume. Although it does not explicitly say the operation is side-effect-free, the verbs 'multiplies', 'shows', and 'computes' make the pure-calculation nature clear.

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?

A single sentence front-loads the main purpose and packs the calculation behavior, output types, and target-volume use case into one clause. There are no wasted words and no duplication of schema content.

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 calculator whose schema is fully described, this is nearly complete: the agent knows the process order, what the tool computes, and the output concepts. The only notable gap is that the description does not state what happens when target_output_m3 is omitted, leaving the forward-only mode to the schema's 'reverse mode' hint.

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 schema already documents yield_pct, label, and target_output_m3 at 100% coverage, so the description is not required to restate them. It adds conceptual color by linking stages to the sawing->drying->machining->finishing chain, but it does not materially change how the parameters should be filled in.

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 object: 'cumulative yield through a wood production chain' and names the exact calculation behavior (multiplying stage yields, per-stage loss, roundwood volume for a target). This is clearly distinct from the sibling calculator tools, which handle single measurements or material estimates.

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 states the context of a multi-stage wood production chain, which implies when the tool is relevant. However, it never says when not to use it or points to a sibling alternative such as sawmill_recovery or rough_sizing, so the agent has to infer the boundary.

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 must fully disclose behavior, but it only states 'Weight of metal stock per profile' without explaining return units, whether qty multiplies the result, or which dimensions are required per profile. It does not mention that this is a pure calculation with no side effects, leaving room for misinterpretation about output semantics.

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 a single, front-loaded sentence that efficiently states the tool's purpose and scope. It avoids fluff and organizes profiles and materials clearly, though it could be expanded to include parameter mapping without losing conciseness.

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 tool is complex with 9 parameters and multiple profile-specific dimension requirements, but the description is too sparse to guide correct invocation. It does not explain the relationship between profile and parameters, nor the output format. An agent selecting this tool would likely struggle to know which dimensions to provide for a given profile.

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 description adds little beyond the schema's parameter descriptions. It lists profile names that already exist as enums, but does not clarify which parameters are needed for each profile (e.g., pipe needs od_mm and wall_mm, beam needs height_mm and web_mm). With 67% schema coverage, the description does not compensate for the missing qty, profile, and material descriptions.

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 weight of metal stock and enumerates the supported profiles and materials, which distinguishes it from sibling tools like lumber_weight_calculator or green_log_weight. It names specific resources (pipe, round bar, etc.) and the implied verb 'calculate' is unambiguous.

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 gives clear context: it is for metal stock weight per profile, which implies when to use it. However, it does not explicitly mention exclusions or alternatives (e.g., lumber_weight_calculator for wood), so it lacks explicit vs.-alternative routing. The context is sufficient for correct selection among siblings.

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 are provided, so the description bears the full burden of behavioral disclosure. It only says 'miter angles three ways' and gives one behavioral detail: crown mode returns miter + bevel. It does not describe the output format, the units used, or how invalid mode/parameter combinations are handled. For a mode-based calculator with no output schema, this is an important gap.

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 entire description is one efficiently structured sentence. It front-loads the core purpose ('Miter angles three ways') and then gives compact, non-redundant parentheticals for each mode. No filler, no repetition of the schema enum values, and every clause contributes to the meaning.

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-mode calculator with 100% schema coverage, the description leaves two important gaps: it never states that only the parameter relevant to the selected mode should be supplied (an agent could pass sides along with corner_angle_deg), and it does not describe what the tool returns. These are noteworthy omissions for a tool that relies on mode-specific parameters, so the description is adequate 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's mode references ('crown mode... spring angle') mirror the schema's own parameter descriptions and do not add new syntax, units, or format details. It helps pair modes with parameters, but that information is already present in the schema, so the description adds no semantic value beyond the baseline.

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 that the tool computes miter angles and enumerates three distinct modes (corner, sides, crown) with brief scenario explanations. This goes beyond the name and distinguishes it from the many other calculators in the sibling list, though the verb 'Miter' is a shorthand for 'calculates miter angles' rather than a fully explicit phrasing.

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?

Each mode comes with a situational cue (corner angle between two pieces, regular polygon with N sides, crown molding at a spring angle), which helps an agent select the right mode for a request. However, there is no explicit guidance on when to prefer this tool over alternatives or when not to use it. Since no sibling is closely related, the lack of explicit exclusions is not critical but still leaves the when-to-use implied rather than stated.

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 provided, the description carries the full burden of behavioral disclosure. It communicates the calculation types, the optional density-driven outputs, and the basis behavior, which is solid for a pure calculator. It does not describe the return format or state that inputs are validated, but it does not need to discuss side effects or destructive behavior for this kind of 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 compact and front-loaded: it states the formula source first, then the mode behaviors, then the optional basis behavior. Every sentence contributes either mode guidance, output behavior, or default context, with no filler or repetition.

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 an 8-parameter, 3-mode tool with no output schema and no annotations, the description covers the main behavioral surface well: modes, basis, formula source, and optional outputs. The main gap is that the 'max' mode is only implied via 'Optional Gb' rather than explicitly linked to the schema's mode='max' value, and required parameters per mode are left to the schema descriptions.

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 75%, and the description adds semantic grouping that the raw schema lacks: wet+dry masses map to sample mode, start/target MC maps to drying mode, and Gb maps to MCmax/MCsink. It does not enumerate every parameter, but the schema already documents each one, and the description clarifies how the parameters relate to the modes.

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 identifies the domain (wood moisture content) and gives specific mode semantics: sample-based MC_od/MC_wb from wet+dry masses, and drying outcome with mass/water lost. It distinguishes itself from sibling calculators like wood_emc and wood_shrinkage by its explicit formula reference and mode breakdown. However, it says 'Two modes' while the schema exposes three mode values, and it never explicitly names the 'max' mode, which slightly weakens precision.

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 gives clear usage context by explaining the two primary computation modes, the optional Gb-based MCmax/MCsink output, and the od/wb basis with od noted as the industry default. It does not explicitly mention alternative sibling tools or exclusion conditions, so it stops short of full routing guidance, but the mode descriptions are enough for an agent to pick the right invocation path.

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 core calculation formulas, transfer-efficiency ranges, and the optional extra for 3D surfaces. However, it does not state what the tool returns (e.g., litres, cost, or both), how conflicting mode inputs are resolved, or other edge-case behavior.

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: a short domain statement followed by the technical formulas and the optional-surface modifier. It is appropriately front-loaded and contains little waste, though the first sentence is a noun phrase rather than a full actionable sentence.

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 an 8-parameter calculation tool with no annotations and no output schema, the description covers the main formulas and mode distinction. It is not fully complete because it omits output units, the purpose of price_per_l, and how the tool behaves when both theoretical and data-sheet coverage inputs are supplied.

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 baseline is 3, but the description adds meaningful semantics beyond the schema: it groups parameters into a theoretical mode and a data-sheet mode and supplies typical transfer-efficiency values. It could be stronger by explaining the role of price_per_l and required mode combinations, but it clearly adds 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?

The description clearly identifies the resource as 'wood finishing paint quantity' and gives the calculation route, so an agent can tell this is a paint-coverage calculator. It lacks an explicit verb like 'calculate' and does not compare itself to sibling calculators, so it falls just short of a 5.

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 usage context through the formula and the 'or a data-sheet coverage' alternative, giving two calculation modes. It does not explicitly state when to choose this tool over siblings or when to prefer one mode over the other, so guidance 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?

With no annotations, the description must carry behavioral disclosure. It states the calculation rule (d2/162), the outputs, and unit support, which are useful. However, it does not mention side effects, whether it modifies data, error handling, or rounding behavior. For a calculation tool, read-only is implied but not stated, so a 3 is appropriate.

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 exactly two sentences with no redundancy. It front-loads the purpose and output, then adds the unit support note. Every word contributes to understanding, making it highly concise and well-structured.

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, so the description must convey what is returned; it does by listing total length, weight, and bars per tonne. It also specifies the input (list of rebar lines) and the rule. It lacks details on output formatting or edge cases, but for a straightforward calculation, it is sufficiently 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 high (100%), with the lines parameter described as 'Rebar lines' and diameter_mm described with an example. The description adds value by clarifying support for both metric and US # sizes as diameters, which is not explicit in the schema. This enriches the meaning of the diameter_mm parameter, justifying a 4.

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's function: it performs rebar weight takeoff using the d2/162 rule, returning total length, weight in kg, and bars per tonne for a list of rebar lines. It also mentions support for metric mm and US # sizes, making it specific and distinct from the sibling calculator tools.

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 usage for rebar quantity takeoff by describing the input and output. It does not explicitly exclude alternatives or name sibling tools, but given that none of the 36 siblings are rebar-related, the context is clear enough. It lacks explicit 'when to use' phrasing, but the purpose is self-evident.

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 transparency burden. It discloses the core calculation behavior: adding planing allowance at 2.5 mm/face, saw kerf at 2.2 mm, applying WH-2021 Eq. 4-9 shrinkage, and mapping direction to board width/thickness. It does not describe return output or edge cases, but the main behavior is clear.

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 tight sentences with no filler: purpose, defaults, and direction mapping are front-loaded. Every sentence earns its place.

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 tool with 11 parameters, no annotations, and no output schema, the description captures the algorithm but does not state the forward/reverse mode flow, the shape of the result, or the optional species/custom shrinkage path. The schema compensates for parameter details, making this adequate but not fully self-contained.

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 82%, so the baseline is 3. The description adds meaning beyond the schema by supplying default values for planing and kerf and by explaining the physical interpretation of tangential vs radial. It leaves mode and custom-species behavior to the schema, which already documents them adequately.

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 begins with a precise verb and object: 'convert between finished (planed, kiln-dried) and rough-sawn dimensions'. It distinguishes this from sibling calculators by naming the specific allowances and shrinkage equation, so an agent can tell it apart from tools like board_feet_calculator or wood_shrinkage.

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 provides clear timing context ('before drying and planing') that implies when this sizing conversion is needed. It does not explicitly name sibling alternatives or give a when-not-to-use condition, so it falls short of a 5.

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 of behavioral disclosure. It mentions the inclusion of a web_url with the calculation prefilled, which is a useful behavioral trait, but it does not specify the full return format (e.g., whether the numeric RPM is also returned) or any other side effects. It is adequate but incomplete for a tool with no annotation support.

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 two sentences, front-loading the core purpose and formula, and includes the essential unit distinctions. No filler or repetition; it earns its length.

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 tool is simple, but the absence of an output schema means the description should clarify what the caller receives. It mentions a web_url but does not confirm whether the numeric RPM is also returned or how the URL is used. For an agent to invoke and parse the result correctly, this is a notable gap.

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 parameters are already well documented. The description adds the formulas and reinforces unit conventions (mm/inches, m/min/SFM), which is helpful but does not fundamentally expand beyond the schema. This matches the baseline of 3 for high 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 spindle speed (RPM) from cutting speed and provides the exact formulas. It distinguishes itself from sibling tools like cutting_speed_calculator by implying an inverse relationship, though it does not explicitly name alternatives.

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?

There is no guidance on when to use this tool versus alternatives such as cutting_speed_calculator or cnc_feeds_speeds. The description explains the math and units but does not state scenarios where this tool is the correct choice, leaving selection to inference.

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 present, the description carries the behavioral disclosure burden. It does disclose the core computation, the benchmark context, and a useful behavioral expectation ('Doyle underestimates small logs, so overrun is usually positive'). However, it does not explain edge cases, output format, or how the benchmark comparison is presented, so transparency is only partial.

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 two sentences with no filler. It front-loads the primary formula and benchmark scope, then adds the secondary overrun calculation and a compact interpretive note. Every clause contributes either a formula, a benchmark context, or a useful behavioral expectation.

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 two-required-parameter calculator with complete schema coverage, the essential formulas are present. But with no output schema, the description does not state what the tool returns (e.g., a fraction, percentage, or benchmark classification), nor does it resolve the ambiguity of the FAO/EU/SE-Asia reference ranges. It is adequate for basic invocation but not fully self-contained.

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 baseline is 3, but the description adds real semantic value by relating the required parameters through the recovery-rate formula and by defining how actual_bf and log_rule_bf combine in the overrun calculation. The Doyle note also gives qualitative interpretive context for those optional parameters.

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 identifies the tool as a sawmill recovery-rate calculator with an explicit formula: sawn volume out / roundwood volume in. It also describes the secondary overrun/underrun calculation. However, it never names or differentiates from sibling tools such as board_feet_calculator or log_volume, so it stops short of full sibling distinction.

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 gives no explicit guidance on when to use this tool versus alternatives like log_volume or board_feet_calculator. It also does not state when the optional actual_bf/log_rule_bf pair should or should not be supplied, beyond the schema's 'optional' label. The intended use case is implied by the formula, but not stated.

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 provided, the description carries the full burden of behavioral disclosure. It transparently lists what the tool returns: sheets used, piece positions, utilization, estimated cut length/count, unplaced pieces, and a web_url for visual layout. It also mentions key configuration options (kerf, rotation lock, trim, multiple stock sizes). It does not discuss side effects or limitations, but as a computation/optimization tool, none are expected. The disclosure is adequate for an agent to understand behavior.

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 two sentences, front-loaded with the core purpose and key capabilities, followed by a concise list of outputs and a note about the visual URL. Every sentence earns its place; there is no redundancy or fluff. The structure makes it easy to scan and understand quickly.

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?

Without an output schema, the description must explain return values, and it does: sheets used, piece positions, utilization, estimated cut length/count, unplaced pieces, and a web_url. It also covers the main input options. For a complex tool with nine parameters, the description plus the fully documented schema provide sufficient information for an agent to call it correctly. It does not detail edge cases or error handling, but those are typically outside the scope of a tool description.

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 parameters are already documented. The description adds only light context, such as linking 'rotation lock' to 'grain direction' and 'trim' to 'damaged edges', which marginally enhances understanding. It does not provide new parameter syntax or formatting details beyond what the schema already covers. Given the high schema coverage, a baseline of 3 is appropriate; the description does not compensate with additional semantic 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?

The description clearly states the tool optimizes 2D guillotine cutting plans for sheet materials like plywood, MDF, glass, and sheet metal, listing key features (kerf, rotation lock, trim, multiple stock sizes). This verb+resource+scope definition distinguishes it from the sibling linear_cutting_optimizer, which handles 1D cutting. The purpose is unambiguous and specific.

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 establishes the tool's domain explicitly ('2D guillotine cutting plans for plywood, MDF, glass, sheet metal'), making it clear when it applies. However, it does not explicitly name alternatives or state when NOT to use it, such as pointing to linear_cutting_optimizer for one-dimensional cuts. The context is clear enough for an agent to infer correct usage, but explicit exclusions are missing.

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 of explaining behavior, and it does state the core conversion directions and material coverage. It does not disclose how qty, width_mm, and length_mm factor into the weight calculation, but for a non-mutating calculator tool the core behavior is adequately surfaced.

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 one compact sentence that front-loads the main purpose and enumerates supported materials. There is no redundant wording 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?

Given seven parameters徒 with no annotations and no output schema, the description is too thin. It does not explain how mode selects the input direction, what the default material/dimensions are, or which parameters are needed for weight calculation, so an agent might invoke it incorrectly.

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 only 43%, leaving qty, material, width_mm, and length_mm undocumented in the schema. The description only mentions the material list and conversion modes indirectly, but does not compensate for the missing parameter semantics such as the role of dimensions and quantity in weight computation.

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's specific actions: converting sheet metal gauge to thickness in mm, thickness to gauge, and computing sheet weight for four named materials. It is specific about the resource and verb, though it does not explicitly distinguish itself from sibling tools like metal_weight_calculator.

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 intended use is implied: an agent can infer this tool is for gauge/thickness conversion or sheet weight calculation. However, there is no explicit guidance on when to prefer this over alternatives such as metal_weight_calculator, nor any when-not-to-use conditions.

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 are provided, so the description carries the full burden of behavioral disclosure. It explains the calculation and classification, but does not specify the output format (e.g., whether it returns the SF value, the classification, or both), rounding behavior, or any edge-case handling. For a calculation tool, this is a significant gap that could lead an agent to assume an output structure that is not guaranteed.

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 a single, focused sentence that front-loads the core formula and then adds the classification detail. There is no fluff or repetition; every word contributes to the tool's purpose and scope.

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 two-parameter calculator, the description covers the calculation and classification context. However, it does not specify the output format or what the tool actually returns (e.g., just the SF number, or also the IMO classification). Without an output schema, this missing detail leaves an agent uncertain about the result structure, so the description is 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 coverage is 100% with clear descriptions for both parameters. The description adds the formula (volume/mass) and the classification context, which goes beyond the schema's individual parameter descriptions. This extra semantic context helps the agent understand the relationship between the parameters and the intended use, so it adds value 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 states a specific calculation (volume/mass) and a classification step against IMO CSS Code Table 4.1 for wood cargoes. It clearly distinguishes itself from sibling calculators by naming the exact domain (wood cargo stowage factor) and the reference standard, so an agent knows exactly what this tool does and how it differs from other calculators.

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 usage for wood cargo stowage factor calculations and classification, but does not explicitly mention when to use it over alternatives or when not to use it. However, the purpose is so specific that an agent would naturally select it for this task, and no sibling tool covers stowage factor. It provides clear context (wood cargoes, IMO CSS Code) but lacks explicit exclusions 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?

With no annotations, the description carries the full behavioral burden, and it only mentions that layout pattern influences the waste factor. It does not disclose output format, rounding behavior, how box counts are derived, whether prices are required for cost output, or any assumptions about waste. This is a meaningful transparency gap.

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 one compact sentence that front-loads the core outputs and then enumerates inputs efficiently. Every phrase adds information, and the layout examples are useful without bloating the text.

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?

This is a moderately complex 10-parameter calculator with no output schema, no annotations, and only 30% schema description coverage. The description provides a useful overview but omits important context such as unit conventions, how cost is calculated when only one price type is given, and what the returned estimate contains. It is not complete enough for an agent to confidently predict behavior.

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 only 30%, so the description must compensate by grouping and naming parameters. It does cover the main semantic categories: room dimensions, tile dimensions, grout width, layout-driven waste, box size, and prices. However, it does not clarify the difference between price_per_m2 and price_per_box, nor how waste_percent interacts with layout-specific 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 identifies the resource and outcome clearly: computing tiles, boxes, and cost for a room. It lists the relevant inputs and layout options, making the tool's purpose recognizable. It does not explicitly differentiate from the sibling flooring_calculator, so it loses the top point.

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 guidance is given about when to choose this tool over flooring_calculator, paint_coverage, or other calculators. The description implies use for tiling estimates but does not state prerequisites, exclusions, or when an alternative would be more appropriate.

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 provided, the description carries the full burden and does well by disclosing the return values (gross/net of self-weight, governing mode at L/300, utilization, section properties) and the prefilled web_url. It omits output units and detailed assumptions such as load duration or service class, but the planning-estimate caveat mitigates this somewhat.

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 front-load the core function and outputs, then add the safety caveat and web_url note. Every sentence contributes information, with no filler or repetition.

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 calculator with no output schema and no annotations, the description lists what is returned, defines its scope and limitations, and mentions the prefilled beam URL. It does not state output units or all modeling assumptions, but it is sufficient for an agent to decide whether and how to invoke the 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?

The schema already covers all five parameters at 100%, so the baseline is 3. The description adds useful meaning beyond the schema by specifying 'simply supported', 'center point load', and 'gross and net of self-weight', which clarifies how span, load_type, and beam geometry are interpreted.

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 calculation: 'Allowable load on a simply supported rectangular timber beam' under Eurocode 5/EN 338 conventions, and enumerates concrete outputs such as allowable UDL, center point load, governing mode, utilization, and section properties. This makes the tool's function clear and distinguishable from sibling volume/weight/coverage calculators.

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 clearly frames when to use the tool ('planning estimate') and gives an explicit exclusion: 'NOT a structural design - verify with an engineer.' It does not name alternative sibling tools, so routing among related calculators like rough_sizing is left to inference.

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 are provided, so the description carries the burden, and it does so by disclosing both quantity conversion and price translation behavior plus the key MBF conversion factor. It does not describe output rounding or return shape, but there is no destructive or mutating behavior to warn about.

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?

A single sentence packs the action, unit list, price-normalization behavior, and conversion factor with zero filler. The most important information is 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?

For a 5-parameter tool with no output schema, the description covers the core conversion scope, supported units, price outputs, and a key factor. It doesn't spell out output structure or the interaction of qty and price, but those are partially documented in the schema and are not blocking for correct invocation.

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?

With 80% schema coverage, the schema already documents most parameters, so the baseline is 3. The description adds value by mapping cryptic enum values (hft3, hm3, ht) to full unit names and by clarifying that price is converted to both $/m3 and $/MBF, which gives meaning to the price and price_per parameters.

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 verb ('Convert... and translate'), the exact resource (timber quantities and prices), and enumerates every supported unit. It also includes a concrete conversion factor, so an agent can differentiate this converter from dimension-based siblings like board_feet_calculator or hoppus_volume without opening the schema.

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 tool's use case is clearly stated: when timber quantities need unit conversion or a price must be normalized to $/m3 and $/MBF. It does not explicitly name alternative tools or give when-not-to-use conditions, but the numeric-conversion context is unambiguous among the sibling calculators.

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 behavioral burden. It discloses that pattern repeat and openings are incorporated into the calculation and that roll dimensions can come from presets or custom values, which is useful beyond the tool name.

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 efficient sentences with no filler. The core purpose is front-loaded, and the roll configuration guidance directly follows.

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 is adequate for a moderately complex calculator with 7 parameters and no output schema. It explains what inputs matter and the main calculation considerations, though it could explicitly mention rounding or how the final roll count is expressed.

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 57%, so the description adds meaningful value by grouping parameters into preset rolls vs custom dimensions and by naming the factors (pattern repeat, openings) that correspond to otherwise plain numeric fields. It does not fully describe every parameter, but supplements the schema well.

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 the exact purpose: computing wallpaper rolls needed for a wall. It names the key accounting factors (pattern repeat, openings) and the roll-type options, which clearly distinguishes it from the sibling calculators.

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 gives clear context: use when estimating wallpaper rolls for a wall, and explains how to configure rolls via preset or custom dimensions. It does not explicitly mention alternatives/exclusions, but the domain is unambiguous against the sibling list.

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 of disclosing behavior. It names the Hailwood–Horrobin model, Simpson 1973 coefficients, the desorption branch, and the reverse mode, which is substantial transparency for a calculator tool. It does not state output units or defaults for mode/branch, but the core behavior is clearly disclosed.

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 concise sentences with no filler. The core calculation is stated first, and the optional reverse mode is added as a clear second sentence.

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 tool has two modes and a branch parameter, making it moderately complex, yet the description omits output units, default mode/branch values, and explicit mode-dependent parameter requirements. The schema partially compensates with parameter descriptions and enums, but the description alone is 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 coverage is 60%, so the description needs to add some meaning beyond the schema. It does relate temperature and RH to EMC, and target MC to required RH in reverse mode, but it does not explain the branch parameter's adsorption option or explicitly clarify which parameters are required in each mode.

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 computes equilibrium moisture content from temperature and RH using a specific model, and it also names the reverse mode. It is specific about the resource and inputs, though it does not explicitly distinguish itself from the sibling moisture_content tool.

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 implied rather than explicitly guided: an agent can infer it is for computing EMC from temperature and RH, or RH from a target MC in reverse mode. However, there is no explicit when-to-use or when-not-to-use guidance, and no mention of alternatives among 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 provided, the description carries the full behavioral burden, and it does well by disclosing the formula, the species-table behavior, the custom S_od + FSP path, and the important clamping behavior above FSP. It does not describe the return value format or any edge-case behavior beyond clamping, but for a read-only calculator the disclosed behavior is solid.

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: it leads with the formula, then directions, then species/custom inputs, then the crucial clamping rule. Every sentence contributes distinct information and there is no filler or 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?

The description covers the calculation logic, input modes, and the FSP clamp, which makes the tool usable. However, since there is no output schema, the description does not fully specify what the tool returns — for example, whether it returns a shrinkage percentage, a new dimension in mm, or both — which is a meaningful gap for an agent.

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 86%, so the schema already documents most parameters. The description adds meaningful context beyond the schema by explaining that species are selected from a verified table, custom S_od and FSP can be supplied, and MC is clamped to FSP — all of which clarify how parameters like custom_shrink_od_pct and custom_fsp_pct behave.

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: it computes wood shrinkage between two moisture contents, names the governing formula (WH-2021 Eq. 4-9), and enumerates the three direction modes (tangential, radial, volumetric). This makes the tool's purpose unambiguous and clearly distinguishable from sibling moisture-related tools like wood_emc or moisture_content.

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 gives clear operational context — start MC to end MC, directions, species selection or custom coefficients, and the FSP clamp — so an agent can infer when to use it. However, it never explicitly states when to prefer this tool over alternatives such as wood_emc or moisture_content, nor gives any when-not-to-use guidance.

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