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 analysis scope (geometry-only, uncapped), accepted formats, URL handling (including zip unpacking first model), the base64 truncation caveat, and the local-file upload workaround. It does not mention response format or rate limits, but for a read-only analysis tool this is solid 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 a single dense paragraph, front-loaded with the purpose and metrics. It includes practical usage details without unnecessary fluff. Slightly repetitive with the schema's model_url description, but overall 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?

Given no output schema and no annotations, the description does well to explain what analysis outputs (metrics), input sources, fallback options, and limitations. It lacks an explicit summary of the return format or error behavior, but the listed metrics and scope make the tool's functionality reasonably complete for an agent to use.

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 has 100% property descriptions, so the baseline is 3. The description adds meaningful value by marking model_url as 'PREFERRED', explaining the base64 fallback size limit and truncation risk, and noting that filename is required with base64. This goes beyond what the schema provides.

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

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

The description uses a specific verb ('analyze') and resource ('3D model'), lists concrete metrics (bounding box, volume, triangle count, watertight/manifold, solidity), and explicitly distinguishes itself from printing-specific tools by emphasizing 'beyond 3D printing' and use cases like game-asset QC and AR/VR optimization. This clearly separates it from siblings like check_printability and validate_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 gives clear guidance on when to use the tool ('useful BEYOND 3D printing') and lists specific use cases and accepted input types (URLs, zip files, direct links, etc.). However, it does not explicitly name alternative tools or state when NOT to use it, though the 'beyond printing' framing implies a distinction.

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?

Since no annotations are provided, the description carries the full burden. It discloses the tool is free, fetches remote URLs, unpacks zips (using first printable model), and warns about base64 truncation in the parameter notes. This is strong transparency for a read-only analysis tool, though it doesn't explicitly state no destructive actions, that's implied by 'sanity check'.

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 well-structured: front-loaded with core purpose, followed by outputs, then input methods, and a sibling reference. Every sentence adds value, though it is somewhat long. It earns high marks for efficient communication.

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 (multiple input methods, zip handling, heuristics, relationship to other tools), the description covers all important aspects: what it does, what inputs it accepts, how to handle local files, and when to use it relative to siblings. It doesn't detail the return schema, but the output items are listed and there is no output schema to rely on.

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 baseline is 3. The description enriches parameter semantics beyond the schema by explaining accepted URL formats (Thingiverse, GitHub, Drive, direct links, zip), the zip 'first printable' behavior, and the upload fallback. The base64 truncation warning also adds operational context not present in 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+resource: 'FREE geometry sanity check for an STL/3MF/OBJ/PLY model' and enumerates concrete outputs (bounding box, volume, triangle count, watertight flag, fits-on-bed, heuristics). It explicitly differentiates from sibling tools by pointing to analyze_3d_model for a general framing and to get_print_quote as a following step.

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?

Provides clear usage context: 'Great pre-flight before get_print_quote' and explicitly contrasts with analyze_3d_model. It also gives practical input guidance (URL types, zip behavior, upload workflow) that helps an agent decide when and how to invoke this tool.

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 burden of behavioral disclosure. It reveals important traits: fair-use limits, exact vs heuristic labels in the full report, zip handling (first printable model), base64 corruption risk, and the need to upload local files. This is exceptionally transparent for an API consumer.

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 long but information-dense; every sentence adds value. It is front-loaded with the core purpose and then systematically covers input methods, modes, and limitations. Slight verbosity around the fair-use policy and upload steps could be trimmed, but overall it is well-structured for a complex tool.

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 9 parameters, no output schema, and no annotations, the description is remarkably complete. It explains what the tool returns in both default and full mode (verdict, watertight, dimensions, etc.), covers all input formats including zip and local files, and clarifies parameter dependencies like layer_height overriding quality. The agent can invoke this tool with confidence.

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 the schema already has 100% coverage, the description adds significant meaning beyond it. For example, it explains that 'model_url' is preferred, details how zip files are processed, warns about base64 truncation in MCP clients, and clarifies the 'full' parameter's effect. This enriches the agent's understanding of parameter nuances.

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

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

The description opens with a specific verb+resource: 'FREE 3D-printing DFM / printability check for an STL/3MF/OBJ/PLY model.' This clearly states what the tool does and distinguishes it from sibling tools like slice_model or validate_model by focusing on printability/DFM checks, supported formats, and link-based input.

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 on when to use the default fast mode vs full=true, as well as usage policy (free for personal/interactive use vs prepaid credits for agents). It also explains preferred input methods (model_url vs model_base64). However, it does not explicitly compare against sibling tools or state when NOT to use this tool, so it falls short of the top score.

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 discloses key behaviors: 'FREE', 'Fair-use limited (counts as one slice per printer)', and 'for a .zip (first printable model inside is used)'. It does not explicitly state read-only status or error handling, but the slicing/comparison nature makes side effects unlikely and the rate-limit/zip rules are useful disclosures.

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 dense single paragraph that front-loads the core action and then covers input formats, local file upload, fair-use limits, and a use-case tag. No sentences are wasted; it is somewhat long but justified given the tool's complexity.

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 17 parameters and no output schema, but the schema richly explains parameters. The description explains the output table (print time/filament/cost/fits-on-bed, cheapest/fastest picks) and gives input URL types and the upload workaround. It lacks edge-case handling like invalid URLs or errors, but is sufficient for core usage.

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

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

Schema coverage is 100% and every parameter has a clear description (e.g., printer list examples, model_url formats). The tool description repeats model_url input guidance but adds no new parameter-level meaning beyond what the schema already provides, so baseline 3 is appropriate.

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

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

The description opens with 'Slice the same model across several printers and get a table of print time / filament / cost / fits-on-bed, plus the cheapest and fastest picks.' This names a specific verb (slice/compare), the resource (printers and model), and the output (table), and clearly distinguishes it from sibling tools like slice_model or cost_breakdown.

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 use case: 'Perfect for "which of my printers should I use?"' and explains accepted input URL types plus the local upload workaround. However, it does not explicitly exclude alternatives like cost_breakdown or slice_model, so it lacks definitive 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.

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

With no annotations, the description carries full burden. It discloses fair-use limitation ('one slice'), local file upload workaround, and zip behavior (first printable model used). It also implies server-side fetching by listing URL formats, though it doesn't explicitly describe network requests or side effects. Good but not exhaustive.

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 moderately sized, front-loaded with purpose, then link handling, then fair-use limitation. Each sentence adds meaningful info, though the list of link types is also present in the model_url parameter description, making it slightly redundant. Still 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?

Given 15 parameters and no output schema, the description covers the primary input handling (model URL formats, local file workaround) and key limitation (fair-use). It explains the breakdown components in the purpose sentence, making the return value clear. Adequate for the tool's complexity.

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 covers 100% of parameters with descriptions, so baseline is 3. The description adds overall pricing philosophy ('bring your own pricing') but does not add semantics beyond the schema, especially for the many BYO pricing 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?

Description clearly states the tool breaks a quote into material, machine-time, setup, and margin components using user-specified pricing. This specific verb+resource distinguishes it from siblings like get_print_quote, though it doesn't explicitly name them.

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 by detailing acceptable model link formats and the need for user-provided pricing, but it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. The context is clear but lacks explicit 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 carries full burden and does excellently: discloses server-side fetching/unzipping, first printable model in a zip, base64 truncation risk, fair-use limits, credit/x402 for high volume, and response echoing pricing_basis. These are meaningful behavioral details beyond what the schema provides.

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 paragraph, but every sentence earns its place: output summary, input source options, BYO pricing, and usage limits. It is front-loaded with 'FREE' and the core value proposition. Slightly long but not wasteful for a 17-parameter tool.

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 17 parameters, no output schema, and no annotations, the description covers the essential behavioral context: input methods, pricing customization, fair-use limits, and the fact that output includes print time, filament grams, and priced quote. It could mention error cases or defaults, but schema covers parameter defaults, making this 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 coverage is 100%, so baseline is 3. The description adds value by grouping BYO pricing parameters as optional, clarifying model_url as preferred, warning about model_base64 size limits, and explaining that pricing_basis reflects default vs custom. This goes beyond the individual schema 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 opens with a specific verb+resource: 'Slicer-accurate FDM price quote from a real slice (not an estimate): print time, filament grams, and a priced quote.' This clearly distinguishes it from sibling tools like slice_model or cost_breakdown by emphasizing real slicing and automatic volume discounts.

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?

Provides clear context on how to use the tool: paste a link from supported sources, upload local files first, and optionally supply BYO pricing parameters. It implicitly distinguishes from estimate-based approaches ('not an estimate') but does not explicitly name alternative tools or state when not to use this tool.

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 full behavioral burden. It discloses 'FREE' status, 'fair-use limits,' and 'per-call x402 / prepaid credits' for agents/production, which are critical constraints. It implies read-only via 'Discovery' and 'list,' though it doesn't explicitly state no modifications occur. This adds meaningful value beyond a bare list.

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 ('FREE. Discovery: ...') and efficiently communicates contents, usage, and pricing. Every sentence earns its place 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?

Despite no output schema, the description enumerates return fields (id, name, bed/build volume mm, nozzle), lists all 12 materials with variants, and explains the pricing model. It also provides usage guidance ('pick a printer and material id'), making it self-sufficient for an initial discovery tool. The sibling context suggests a rich ecosystem, and this description positions the tool appropriately.

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 is empty (0 parameters), so there are no parameter semantics to clarify. However, the description explains that the output provides `printer` and `material` ids for use in subsequent calls, giving meaning to the return values. This exceeds the baseline for 0 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 states 'List 3D printers, materials and pricing' with specific details (12-printer FDM fleet, 12 filament materials), making the purpose explicit. The term 'Discovery' and phrase 'Call this FIRST' distinguish it from sibling tools like compare_printers and recommend_material, which are specialized. It uses a specific verb+resource structure.

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

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

It explicitly says 'Call this FIRST to pick a printer and material id,' establishing clear when-to-use guidance. It also differentiates free personal/interactive use from paid agent/production use, guiding cost-conscious usage. While alternatives aren't named, its role as the initial discovery step is unambiguous.

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. It thoroughly discloses behavioral traits: free tier with fair-use limit counting as ~4 slices, actual slicing of up to 4 orientations with automatic supports, exact support material measurement, and output of filament/time saved plus estimated price. It also explains input handling (zip → first printable model, base64 truncation fallback) and the upload workflow, providing rich context beyond the 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?

The description is dense but every sentence earns its place: cost/limitations, core function, input formats, fallback path, benefits, and enterprise use. It is front-loaded with the most critical info (cost and purpose). No fluff or 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?

Given there is no output schema, the description compensates by summarizing the output: recommendation, support material, filament/time saved, and estimated price. It covers input types, fallback, and cost. Minor gaps remain (e.g., error handling, max URL size limits) but overall it is sufficiently complete for a tool of this complexity.

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 baseline is 3. The description adds extra meaning for model_url and model_base64 by explaining the upload flow, zip handling, and base64 truncation risks for MCP clients. It also clarifies that layer_height overrides quality. This exceeds the schema's descriptions, earning 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 is an 'Orientation optimiser' that 'slices up to 4 candidate build orientations' and 'recommends the one using the LEAST support material (exact, slice-derived)'. This is a specific verb+resource with a clear scope, and it distinguishes itself from sibling tools like slice_model or check_printability by focusing on orientation optimization.

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

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

The description implies the use case: reduce support waste, print time, and post-processing. It also mentions fair-use limits and the x402 agent lane for high volume. However, it does not explicitly contrast with alternatives like slice_model or list when not to use it, so it stops short of full exclusion 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 carries the full burden. It discloses useful behavioral context: 'FREE', the default slice/quote behavior, zip unpacking ('first printable model inside is used'), and a vague 'Fair-use limited'. However, it does not describe what the output contains, potential failure modes, rate limits, or the meaning of 'fair-use limited', leaving gaps in behavioral transparency.

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

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

The description is about four sentences, front-loaded with the core purpose and supported with practical input instructions. It has no filler, though some of the input guidance duplicates schema descriptions, which slightly reduces efficiency.

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 15 parameters and no output schema, the description explains core behavior and input options well, but it omits any description of the return value or output format. The phrase 'Fair-use limited' is too vague to be actionable, and there is no note about error handling for invalid models, making it only moderately 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 mostly repeats schema details (use_case enum values, model_url formats, upload URL) rather than adding new meaning. It does clarify the default behavior of the quote parameter, but that is also in the schema, so no additional value is provided.

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 ('Suggest') and a clear resource ('best filament(s) for a use case'), and uniquely mentions it also performs a slice/quote per candidate, distinguishing it from sibling tools like recommend_settings or slice_model. It also lists concrete use_case categories, making the tool's purpose immediately clear.

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 clearly implies when to use the tool by listing use_case categories and giving detailed input instructions (URL formats, upload workflow). However, it does not explicitly state when not to use it or mention alternative tools for other purposes, so it stops short of full exclusionary 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 carries the full burden. It discloses that the tool is FREE, has fair-use limits, fetches the provided URL (unzipping if needed, using the first printable model), and slices with the recommended settings. This adds meaningful behavior beyond a basic 'recommend settings' description, though it doesn't cover potential side effects like storage or rate limit specifics beyond 'fair-use limited'.

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 function and 'FREE'. It is efficient, but the second sentence lists URL types that are also duplicated in the model_url parameter description, which is somewhat redundant. Still, every sentence contributes to understanding the tool's scope and input requirements.

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 14 parameters and no output schema, the description provides a coherent high-level explanation: what it does, how to provide a model, and what you get (time/filament/cost). It doesn't explain the pricing override parameters (margin, setup_fee, etc.) but those are documented in the schema. The description is sufficient for understanding the tool's role and typical use.

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 14 parameters already have descriptions. The tool description adds marginal semantic value over the schema—it reinforces the model_url parameter's supported URL types and the overall workflow, but does not explain any parameter nuances beyond what schema descriptions already provide. Baseline 3 applies.

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

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

The description clearly states the tool suggests layer height, infill, and walls for a specific goal (strength, speed, surface, cost) and then slices the model with those settings to show resulting time/filament/cost. This is a specific verb+resource+goal and distinguishes it from siblings like slice_model (which likely only slices) and recommend_material (which only suggests material).

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: use this tool when you want recommended slicer settings for a goal and see the resulting metrics. It also explains how to provide a model via URL (with specific supported sources) and that local files must be uploaded first. It doesn't explicitly name alternative tools or state when NOT to use it, but the scenario is well understood.

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 transparency burden, and it does well: it states the service is 'FREE,' warns of 'fair-use limited,' clarifies that 'dimensions are exact; time/filament are estimated from the volume factor off one real slice,' and explains that zips use the first printable model and that the server fetches/unpacks links.

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 core purpose and target syntax, then proceeds to input methods and caveats. It is somewhat long and repeats model_url details already present in the schema, but each sentence adds useful operational context or clarity.

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 16 parameters, no annotations, and no output schema, the description provides adequate operational context: what it reports, how to specify targets, how to supply models, estimation caveats, and fair-use limits. It could mention more about expected output structure, but the description covers the key usage scenarios.

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 baseline is 3, but the description adds meaningful value by explaining 'fit-bed' as 'largest uniform scale that fits the printer' and giving concrete examples like 'percent:150' and 'dim:z:80.' It also reinforces model_url semantics with supported link types.

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

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

The description opens with a specific verb+resource: 'Report the new dimensions / time / filament / cost after scaling a model.' It precisely defines the three target modes (fit-bed, percent, dim) and clearly distinguishes this tool from siblings like slice_model or whatif_infill by focusing on scaling outcomes.

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 for when to use the tool: any time a user needs scaled dimensions/cost estimates. It also covers input methods (paste link, local upload, zip handling) and target syntax, though it does not explicitly name sibling tools as alternatives or state 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, the description takes full responsibility. It details the free metrics vs paid deliverables, prepaid credit drawdown (not a new charge), x402 fallback for autonomous agents, fair-use limits, server-side fetching/unzipping, and the upload requirement. This is exceptionally 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 a dense paragraph but front-loaded with the core action, then organized into metrics, input formats, and payment details. Every sentence adds value for a tool of this complexity, though it is not as minimal as a two-liner.

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 21-parameter tool with no output schema, the description covers the main outputs (metric names, G-code/preview formats), all input submission routes, and the payment/credit behavior. It gives sufficient context to use the tool correctly, even for autonomous agents.

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?

All 21 parameters have thorough schema descriptions (100% coverage). The tool description largely reiterates api_key, model_url, include_gcode, and include_preview semantics rather than adding new parameter-level meaning, so baseline 3 is appropriate.

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

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

The description opens with 'Slice on any of the 12 FDM printers,' clearly stating the verb and resource scope. It distinguishes between free metrics and paid deliverables, setting it apart from sibling tools like get_print_quote or analyze_3d_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?

Clear context: it explains when to use the tool (to slice a model and obtain metrics or files), how to provide the model (URL types, upload fallback), and the cost model. However, it does not explicitly mention when not to use it or point to alternative sibling tools.

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

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

With no annotations, the description carries the full burden of disclosing behavior. It reveals that it is free, fast, uncapped, one-shot, geometry-only, explains how URLs and zips are handled, and mentions the local-file upload workflow. It falls short of describing error cases or exact output format, but it covers the key behavioral traits well.

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 (about 80 words) and front-loaded with the key purpose and value proposition. It includes necessary input instructions without fluff, though the list of URL types could be considered slightly dense but still relevant.

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 absence of an output schema and annotations, the description provides sufficient context: what the tool does, the checks it performs, input options, and a hint at the verdict output. It lacks details on edge cases, but for a simple validation tool, it is adequately 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 text does not add additional parameter semantics beyond what each parameter's description already provides; it repeats the URL guidance but doesn't deepen it.

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 ('validate') and resource ('model'), and clearly enumerates what the tool checks: watertightness, wall thickness, size vs bed, and a printability verdict. It also distinguishes itself from siblings with 'one-shot' and 'Geometry-only (no slice)', making its purpose unmistakable.

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 ('FREE, fast, uncapped', 'one-shot') and what it won't do ('Geometry-only (no slice)'). It does not explicitly name alternative tools, but the context is sufficient to infer its role for quick printability checks.

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 disclosure burden. It mentions 'FREE' and 'Fair-use limited (one slice per infill value)' which are useful behavioral traits. However, it does not elaborate on fetching behavior, error cases, or performance, leaving some unknown aspects. The schema's model_url description covers server fetching, but the description itself adds limited transparency beyond that.

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

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

The description is three sentences long, front-loaded with the core purpose and immediately followed by model input instructions and limitations. Every sentence earns its place—no redundant or vague wording.

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 adequately compensates for the lack of an output schema by explicitly stating the output is a table of print time, filament, and cost across infill percentages. It also covers model sourcing options and fair-use limits. It does not detail the exact table formatting, but given the simplicity, the information is sufficient.

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?

Since the input schema has 100% description coverage for all 15 parameters, the baseline is 3. The tool description does not add any parameter-level meaning beyond what the schema already provides, so it neither helps nor hurts.

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 produces a comparison table of print time, filament, and cost across multiple infill percentages, explicitly framing it as a sweep for trading strength against material/time. This distinguishes it from sibling tools like slice_model, which likely performs a single slice.

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 ('so you can trade strength for material/time') and explains how to provide input models (paste link or upload). It does not explicitly name alternative tools or state when not to use it, but the context is sufficient for a competent agent.

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