FabTally 3D-Print Slicer, Quote & DFM

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 paid nature ($0.002 USDC), the payment flow (x402 challenge without payment), and the concrete return metrics. This gives a clear behavioral profile for a read-only analysis tool, though it omits error handling or file-fetching details.

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

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

Two sentences, front-loaded with the most important information (payment, scope, outputs, payment fallback). Every clause earns its place, and there is no redundant filler. The structure is tight 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?

The description covers the key contexts: what it does, what it returns, payment requirements, and the 'no slice' scope. However, it does not explain how this tool relates to check_printability (both may involve print checks) or define bed-fitting criteria, leaving a minor completeness gap for a tool with no annotations or 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?

The input schema covers all 5 parameters with descriptions (100% coverage), so the baseline is 3. The description adds context like 'optional printer' and 'x402 payment payload' but does not meaningfully enhance the parameter meanings beyond what the schema already provides. It reinforces, but does not add significant 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 computes geometric-only metrics (bounding box, volume, triangle count, watertight flag, fits-on-bed check), and explicitly distinguishes itself from slicing with 'no slice'. This differentiates it from the sibling slice_model and provides a specific verb-resource pair beyond the title.

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 'Cheap sanity check for an STL/3MF/OBJ model' implies when to use it, and 'no slice' hints not to use it for slicing. However, it does not explicitly name alternatives like check_printability or state when a different tool would be more appropriate. 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?

No annotations are present, so the description carries full burden. It richly discloses behavioral traits: free preview vs paid report, the exact artifacts (watertight check, wall-thickness vs 0.4mm nozzle, overhang heuristic), the x402 challenge/payment flow, and the distinction between exact slice-derived and heuristic values. It also explicitly states that omitting x_payment returns the challenge.

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 front-loaded with the primary purpose and packs a lot of valuable details into one dense paragraph. While it's long, every clause adds relevant context (free/paid, payment, exact-vs-heuristic, intended use), so there's no filler. It could be broken into shorter sentences for scannability, but it's still appropriately sized.

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 10 parameters, no output schema, and no annotations, the description does a strong job explaining the free/paid modes, what each returns, the payment challenge, and expected input formats (STL/3MF/OBJ). It lacks an explicit response structure/field list and doesn't discuss error cases or model size limits, but it's largely complete for 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 already covers 100% of the 10 parameters with descriptions, so baseline is 3. The description adds meaningful parameter semantics by explaining that full=false is free, full=true is the paid mode requiring $0.01, and x_payment is used to settle the x402 challenge, which supplements 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 opens with '3D printing DFM / design-for-manufacturability & printability check for an STL/3MF/OBJ model', clearly naming a specific verb ('check') and resource (3D model). It distinguishes itself from siblings like slice_model and get_print_quote by stating it is 'Great before quoting or slicing' and enumerating the specific defects it catches.

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 a clear trigger ('before quoting or slicing') and details the free vs paid workflows, including when payment is needed. However, it doesn't explicitly name alternative tools or state when not to use this tool, so it falls short of the 'explicit alternatives/exclusions' bar.

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 clearly states the cost ($0.005 USDC on Base), the method (real OrcaSlicer slice, not estimate), the outputs (print time, filament grams, priced quote with discounts), and the behavior without payment (returns x402 challenge with price and how to pay). This is a comprehensive and honest disclosure.

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

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

The description is two sentences long, front-loaded with the most critical information (PAID, cost, method), and every sentence adds value: the first explains what the tool does and the second explains the input and payment flow. There is zero redundancy 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?

Despite lacking an output schema and annotations, the description covers all essential context: the cost, the payment challenge mechanism, the input formats, the exact outputs (print time, grams, priced quote), and the volume discount behavior. For a tool of this complexity, it is complete and 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. The description adds minimal extra parameter context: it mentions file types and the payment flow implications of x_payment, but these are largely already covered by the schema property descriptions. It does not deeply explain parameter interactions beyond what the schema provides, so a 3 is appropriate.

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

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

The description uses a specific verb ('Get') and resource ('3D printing price quote'), and clearly states it returns a slicer-accurate quote with print time, filament grams, and a priced breakdown. It distinguishes itself from sibling tools by emphasizing 'real OrcaSlicer slice' and 'not an estimate', which separates it from slice_model and analyze_model.

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: it is a paid tool with an x402 challenge flow, and it tells the user to upload an STL/3MF/OBJ/STEP model. However, it does not explicitly say when to use this tool versus alternatives like slice_model or optimize_orientation, nor does it name a when-not-to-use scenario. The payment flow is well explained, but differentiation from siblings is implicit.

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

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

With no annotations, the description carries the full burden. It openly states it is 'FREE', 'Discovery', and 'returns' data, clearly implying a read-only, non-destructive operation. However, it does not explicitly state there are no side effects or mutations, though the nature of a list/discovery tool makes that highly likely.

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 efficient and well-structured: two sentences, front-loaded with 'FREE' and 'Discovery', and every detail is useful. It packs significant information about contents and usage without redundancy.

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 zero-parameter, no-output-schema tool, the description is fully complete: it names what is returned (printers, materials, pricing), quantifies it (12, 12, x402), explains the free nature, and gives clear next-step guidance. Given the low complexity, nothing important is missing.

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

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

There are zero parameters, so the baseline is 4. The description adds value by explaining the purpose of the return values ('choose a printer and material id') and how they will be used in downstream operations, exceeding the baseline by providing actionable output semantics.

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 lists 3D printers, materials, and pricing, and expands with specific details (12-printer FDM fleet, 12 filament materials, x402 pricing tiers). It uses a specific verb ('List') and resource, distinguishing it from sibling tools like get_print_quote or slice_model.

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?

Explicitly instructs to 'Call this FIRST to choose a printer and material id before quoting, slicing, or a DFM check.' This gives clear when-to-use guidance and implicitly positions it as a discovery prerequisite, while also noting 'No payment required', clarifying it is a free entry point.

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 fully carries the transparency burden. It discloses the payment requirement ($0.02 USDC on Base), the x402 challenge flow when unpaid, the need to pass x_payment to settle, and the fact that it executes multiple real slices. It also mentions the type of output (recommended orientation and savings report), which is comprehensive for a paid 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 appropriately sized for the tool's complexity. The first sentence clearly states the paid nature and core function, followed by concise details on how it works, cost rationale, benefits, and the payment flow. Every sentence adds unique value and there is no fluff 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?

Given the complexity (9 parameters, no output schema, no annotations), the description covers the essential behavioral context: purpose, payment mechanics, and expected outcome (least-support orientation, savings report). It could have explicitly noted the requirement to provide either model_url or model_base64 to get a real result, but the overall completeness is strong for a paid 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 input schema has 100% parameter description coverage, so the baseline is 3. The tool description adds context about the x_payment flow and that printing 'as-is' is the baseline, but it does not add significant per-parameter semantics beyond the schema's own detailed descriptions. Thus, no credit beyond baseline is warranted.

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 a specific action: optimizing 3D-print orientation by slicing up to 4 candidate orientations and recommending the one with the least support material. This clearly distinguishes it from siblings like slice_model or analyze_model by focusing on orientation optimization with slice-derived results.

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 the tool: when you want to minimize support material, print time, and post-processing. It also notes that it costs more than other tools because it runs multiple real slices, which is a comparative guideline. However, it does not explicitly name alternative tools or state when not to use it, so a small gap remains.

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

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

The description is transparent about the paid nature, including price tiers and the x402 challenge behavior when payment is omitted. It also discloses optional outputs (preview PNG, G-code). With no annotations provided, the description carries the full burden, and it does well, though it could mention rate limits or error handling.

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 four sentences long but packed with critical information: payment, pricing tiers, outputs, and upload format. It is front-loaded with the paid notice and uses a logical structure. While information-dense, it remains readable and avoids 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?

Given the tool's complexity (12 parameters, no output schema, no annotations), the description covers the essential context: payment flow, output types, and model input requirements. It does not detail the response JSON structure, but it names the key return fields (print time, grams, layers, geometry, fits-on-bed), which is sufficient for an agent to understand the tool's 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 description coverage is 100%, so the schema already describes every parameter thoroughly. The description adds context about price tiers and upload formats, but does not significantly enhance parameter understanding beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description clearly states the tool's function: 'Full OrcaSlicer slice on any of the 12 FDM printers' with specific outputs (print time, filament grams, layer count, geometry, fits-on-bed). This distinguishes it from siblings like check_printability or get_print_quote, which focus on different aspects of the 3D printing workflow.

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 (when a full slice is needed) and what inputs are required (upload an STL/3MF/OBJ/STEP model). However, it does not explicitly mention alternatives or exclusions relative to sibling tools, 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.