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

No annotations are provided, so the description must disclose behavioral details. It states it adds a rig but does not mention side effects (e.g., whether existing lights are affected, what light types are created, or how they are positioned). This is insufficient for a mutation 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 extremely concise: one sentence stating the purpose followed by a clean parameter list. Every sentence adds value, and the structure is well 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?

The description covers the purpose and parameters adequately but omits details about the created lights (e.g., type, distance from subject, arrangement). For a simple tool this is acceptable, but more completeness would improve agent understanding.

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 0%, so the description must explain parameters. It does so thoroughly: subject_name details default behavior, and all energy parameters include units (watts) and defaults, adding significant meaning beyond the schema's raw titles.

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 'Add a classic 3-point lighting rig' with specific components (key, fill, back/rim), distinguishing it from sibling 'add_light' which adds a single light. The verb 'add' and resource 'lighting rig' are well-defined.

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 indicates when to use this tool (for a 3-point lighting setup centered on a subject) but does not explicitly state when not to use it or compare with alternatives like 'add_light'. The context is clear but lacks 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?

The parameter list mentions 'Value(s) to set before keying', which indicates the tool modifies the property value and then inserts a keyframe. However, there is no disclosure of potential side effects, such as handling invalid data paths, overriding existing keyframes, or any error behavior. Since no annotations are present, the description carries the full burden but only partially reveals 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, with a clear one-sentence purpose followed by a structured parameter list. It avoids unnecessary detail and is well-organized for quick parsing.

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 a straightforward action with no output schema, so not describing return values is acceptable. The parameter list provides enough context for typical usage. However, it omits any mention of prerequisites (e.g., object existence) or constraints on property paths, which could be useful but are not critical.

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?

Even though the schema has no parameter descriptions, the tool description explicitly explains each parameter: 'Object name', 'Property to key', 'Frame number', and 'Value(s) to set before keying', including the rotation degrees conversion note. This fully covers the semantics of all four 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 states the action ('Insert an animation keyframe') and the target ('an object property'), using a specific verb and resource. While it doesn't explicitly differentiate from siblings like 'set_frame' or transform tools, the verb 'insert keyframe' is distinct enough for an agent to identify the 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?

No guidance is provided on when to use this tool versus alternatives. For example, it doesn't mention that this is for animating properties over time or that direct value changes without keyframing might be done via other tools. This leaves the agent without explicit usage 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 full burden but fails to disclose side effects, behavior on invalid inputs, or whether it modifies existing lights vs adds new ones.

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 efficiently structured with a brief statement and parameter list, no unnecessary text, though it could be slightly more compact.

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?

Covers all 6 parameters with defaults and types, but lacks information about the output or side effects in the scene; adequate but not comprehensive.

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?

Despite 0% schema coverage, the description adds meaningful format details for parameters like location ('Comma-separated x,y,z') and color ('Comma-separated r,g,b in 0..1 range'), though it does not explain all parameters in depth.

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 'Add a light to the Blender scene' with a list of parameters, distinguishing it from siblings like 'add_3point_lighting' which adds multiple lights.

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 on when to use this tool versus alternatives like 'add_3point_lighting', no prerequisites or when-not-to-use conditions are provided.

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

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

No annotations are provided, so the description carries the full burden. It discloses the additive nature but lacks details on side effects, prerequisites (e.g., object must exist), error handling, or what happens to existing modifiers. The return value is mentioned, but behavior under failure is absent.

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 relatively concise and well-structured with a bullet list of parameters. However, the list of modifier types could be shortened or referenced externally, but it remains informative and easy to parse.

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 (4 parameters, no output schema, no annotations), the description covers parameters and return value but lacks preconditions (object existence, selection state), error scenarios, and potential conflicts with existing modifiers. It provides a functional understanding but misses important context for robust 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?

The description provides extensive parameter semantics beyond the schema: it explains 'modifier_type' with a list of options, 'modifier_name' as auto-generated if omitted, and gives detailed JSON examples for 'params' across multiple modifier types. This greatly aids an agent in constructing correct inputs.

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 action ('Add a modifier to an object') and resource, with specific modifier type examples. It also distinguishes from the sibling 'apply_modifier' by noting the return value is used for later application.

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 mentioning 'Returns the modifier name so you can reference it later with apply_modifier', but does not explicitly state when to use this tool versus alternatives or provide 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. It states the tool adds a primitive but does not disclose behavioral traits such as whether the new object becomes active, whether it works in specific modes, or if any existing objects are affected. The description is minimally adequate but lacks depth.

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 well-structured. It starts with a clear purpose sentence, then lists parameters in a readable format. Every part adds value, with no wasted words. The parameter list is organized and easy to scan.

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 description lacks mention of the return value (e.g., object name or success status) and prerequisites (e.g., Blender must be running). With no output schema, the agent might need to infer what happens after execution. It is adequate for a basic tool but could be more 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 0%, so the description must compensate. It adds meaning by explaining that location and rotation are comma-separated x,y,z values and lists the primitive types. However, it does not specify units (e.g., degrees for rotation) or constraints (e.g., size must be positive). Overall, it provides useful format guidance 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 clearly states that the tool adds a standard mesh primitive to the Blender scene and lists the specific types available (cube, plane, circle, etc.). This distinguishes it from sibling tools that add lights, modifiers, or perform other actions.

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. For example, it does not mention prerequisites (e.g., Blender must be open) or scenarios where importing a model might be preferred. There is no explicit when-to-use or when-not-to-use information.

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 provided. Description does not reveal behavioral details such as whether objects are modified permanently, if selection is required, or what happens after alignment. Carries full burden but only repeats parameter info.

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?

Extremely concise and efficient. Three lines for parameters with clear formatting. No wasted words.

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 tool with 3 parameters and no output schema, description covers all parameters with enum values. Missing behavioral context (e.g., prerequisites, side effects). Moderate 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 coverage is 0% (no descriptions in schema). Description adds meaningful information: comma-separated names, axis values (X, Y, Z), and alignment options (FIRST, LAST, MIN, MAX, AVERAGE). This compensates for missing 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 states a specific verb 'align' and resource 'multiple objects' origins' clearly. It distinguishes from sibling tools like 'set_origin' (single object) and 'move_object' (move position).

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 on when to use this tool versus alternatives like 'set_origin' or 'move_object'. No when-not or context provided.

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

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

No annotations are provided, so the description carries the full burden. It mentions 'collapsing it into the mesh data,' implying a destructive operation, but does not explicitly warn about irreversibility or side effects on the modifier stack.

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 with two sentences and a bullet list, front-loading the main action. No unnecessary information is present.

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 explains the action and parameters. It mentions the outcome ('collapsing it into the mesh data') but does not specify what happens after application (e.g., feedback, modifier removal from stack). For a simple mutation, this is mostly 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 0%, but the description adds full context: 'name: Object name' and 'modifier_name: Exact modifier name as shown in Blender's Properties panel.' This provides necessary guidance 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 clearly states that the tool applies a named modifier on a mesh object and collapses it into the mesh data. The verb 'Apply' and resource 'modifier on mesh object' are specific, and it distinguishes from sibling tools like 'add_modifier'.

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 (to apply a modifier) but does not explicitly state when not to use or mention alternatives like 'add_modifier'. No guidance on prerequisites or exclusions is provided.

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

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

No annotations are provided, so the description must carry the burden. It does not disclose important behavioral traits such as what happens if the material doesn't exist, if the slot index is out of range, if the operation is destructive (overwrites existing material), or any error conditions. The description only mentions the prerequisite that material must already exist.

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 with one clear purpose sentence followed by a brief parameter list. It is front-loaded and contains no unnecessary information. Could be slightly more structured, but overall efficient.

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 assignment tool, the description lacks completeness. It does not mention return values, side effects, or prerequisites such as the object needing material slots. Given many sibling tools and no output schema, more context is needed to ensure correct usage, especially regarding when to use this versus other material-related tools.

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 value beyond the schema titles by providing brief explanations: 'Target object', 'Material to assign (must already exist)', and 'Material slot index (default 0)'. However, it does not specify constraints like slot must be non-negative or that material_name must match an existing material exactly. Given 0% schema description coverage, the description partially compensates but is not detailed enough for 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 action: assign an existing material to an object's material slot. It uses a specific verb and resource, and the purpose is distinguishable from siblings like create_material or set_object_material_color, 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?

No explicit guidance on when to use this tool versus alternatives. It mentions the material must already exist, but does not provide context such as 'Use after create_material' or 'For changing color, use set_object_material_color instead.' Lacks when-not-to-use or 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?

No annotations provided, so description bears full burden. It discloses that applying deletes the cutter, and distinguishes solver quality/speed. However, it omits that the operation creates a modifier (unless applied) and potential error cases.

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 concise summary followed by a bullet-point parameter list. It is front-loaded with the action and wastes no words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema or annotations exist, so description must cover behavior. It partially explains effects and types but misses key practical details like the modifier nature, required object types, and return value.

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 0%, but the description explicitly explains all 5 parameters with their roles, defaults, and semantics (e.g., solver quality vs speed). This adds significant 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 clearly states it performs a boolean operation between two mesh objects, listing operation types (DIFFERENCE, UNION, INTERSECT) and distinguishing roles of target and cutter. This distinguishes it from sibling tools like join_objects or separate_mesh.

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

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

The description explains the parameters and their effects but does not explicitly state when to use this tool over alternatives, nor does it mention prerequisites like mesh type or non-manifold issues.

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 full burden. It explains the process (capture and stitch) and return value, but doesn't clarify if the tool is read-only or modifies the scene. Given it's a capture tool, the omission is minor but keeps the score at 3.

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

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

The description is short, front-loading the main purpose in the first line. Every sentence is informative and free of 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 appropriately states the return is a single composited image. It covers the key aspects for a capture tool, but could optionally mention image format or resolution.

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 explains both parameters in meaningful terms: 'angles' as a comma-separated list of angle names with a default, and 'max_size' as pixel size per tile. This adds essential context beyond the schema, which only provides types and 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 clearly states the tool captures multiple viewport angles and stitches them into a contact sheet. This distinguishes it from sibling tools like capture_viewport_angle (single angle) and get_viewport_screenshot (single image).

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 usage is implied: use when you need a composite of multiple angles in one image. It doesn't explicitly state when not to use it or mention alternatives, but the context from sibling tools helps.

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 the full burden. It mentions returning an image but does not disclose whether the tool is read-only, affects the scene, or any prerequisites. Basic transparency but lacking 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 concise with two sentences and a parameter list. No unnecessary information, well-structured, and every sentence adds value.

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 and the description covers the core purpose and parameters. Lacks details about image format, error handling, or behavior for invalid angles, but overall sufficient given the tool's simplicity.

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

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

The description adds meaning beyond the schema: it provides an explicit list of valid angles (front, back, left, etc.) that the schema does not specify, and clarifies max_size as 'Maximum pixel dimension'. With 0% schema coverage, this compensation is effective.

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 verb (capture), resource (viewport from a named angle), and output (image). It distinguishes from siblings like get_viewport_screenshot and render_from_camera by specifying the named angle parameter.

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 capturing a specific angle but does not explicitly state when to use this tool versus alternatives like get_viewport_screenshot or render_from_camera. No when-not or alternative guidance is provided.

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

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

The description explains the behavior of both parameters: force (kill vs graceful quit) and save (save changes vs discard without prompt). This gives clear expectations of side effects, especially regarding unsaved changes.

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, using only a few sentences to convey purpose, scope, and parameter behavior. No redundant or extraneous information is present.

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 tool with no output schema, the description provides sufficient context: it explains what it does, when it works, and the effects of each parameter. No additional information is needed for correct 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?

Both parameters (force and save) are fully described in the description, matching the schema. The explanations clarify their boolean meanings and consequences, covering all 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 states the tool's function: closing Blender. It also specifies the types of instances it operates on (those started with start_blender() and user-launched via the addon socket), leaving no ambiguity about its 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 context on when the tool is applicable by distinguishing between managed and user-launched instances. It does not explicitly compare to alternatives, but the scope clarification effectively guides usage.

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 explains the capture-and-composite behavior and the aspect-ratio constraint, which adds useful context. But it does not disclose the output format, whether the operation mutates scene state, or what happens when the reference_name is missing.

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 with the core behavior, followed by a tight parameter list. Every sentence and bullet earns its place with no redundant 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?

The description covers the core invocation and the relationship to a stored reference, which is helpful. However, there is no output schema and the description does not state what the tool returns, what valid angle values are, or how failures are reported, leaving notable gaps for an agent relying solely on this 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?

The schema has 0% descriptive coverage, but the description explains all three parameters meaningfully: reference_name ties to store_reference_image, angle specifies the capture viewpoint, and max_size controls tile size. This fully compensates for the empty schema and gives an agent everything needed to fill the arguments correctly.

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 captures the current viewport from a named angle and composites it with a previously stored reference image. This clearly differentiates it from generic capture tools and diff tools by describing the side-by-side composite output.

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 referencing a name from store_reference_image and mentioning a previously stored reference, so an agent can infer the prerequisite. However, it does not explicitly state when to prefer this over sibling tools like diff_images or capture_viewport_angle, nor does it give 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?

No annotations provided, so description must disclose behavioral traits. It fails to mention side effects (e.g., whether the camera is automatically set as active), prerequisites, or 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?

Description is concise with the main action first, followed by a parameter list. No wasted words, though the parameter list could be integrated more smoothly.

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 incomplete. It does not explain return value, scene requirements, or post-creation 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 0%, so description compensates by explaining all 5 parameters with defaults and types (e.g., 'Comma-separated x,y,z position' for location). However, format details could be clearer (e.g., string vs number).

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 adds a new camera to the Blender scene with a specific verb ('Add') and resource ('camera'). It distinguishes from siblings like add_light or set_active_camera.

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 on when to use this tool versus alternatives like set_active_camera or render_from_camera. The description only lists parameters without context.

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 but only states the basic action. It fails to disclose what happens if the collection already exists, whether it's idempotent, or any side effects on the scene.

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 extremely concise with a single purpose sentence followed by a clear parameter list, containing no unnecessary 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?

For a simple tool with two parameters and no output schema, the description covers the basic action and parameter meanings, but lacks information about return values, error handling, or what happens upon duplicate names.

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?

Despite 0% schema coverage, the description adds value by explaining the name parameter as 'collection name' and the parent_collection as optional with 'nests inside it' behavior, which the schema alone does not convey.

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 verb 'create' and the resource 'collection for scene organisation', which is specific and distinguishes it from sibling tools like move_to_collection.

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 provided on when to use this tool versus alternatives (e.g., when to create a new collection vs. move objects to existing ones), nor any prerequisites or 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?

No annotations are provided, so the description carries the full burden. It mentions 'or replace' hinting at upsert behavior, and 'auto-assign this material to (slot 0)' giving some context, but it does not explain side effects of replacing existing materials, error conditions, or permissions needed. The behavioral description is minimal.

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 clear sentence followed by a well-structured list of parameters. It is front-loaded with the purpose. Slightly verbose but efficient overall.

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 8 parameters and no output schema, the description covers parameters well but lacks information about return values, error handling, or what happens on replacement. It does not explain the 'assign_to' behavior in depth. Adequate but not 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?

The schema description coverage is 0%, meaning the description is the sole source of parameter semantics. It provides detailed information for all 8 parameters, including ranges, defaults, and format (e.g., 'Comma-separated r,g,b in 0..1'). This adds significant value beyond the input schema's titles and 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 starts with 'Create (or replace) a PBR material using Principled BSDF', clearly stating the verb (create/replace) and resource (PBR material). This distinguishes it from sibling tools like 'assign_material' (which applies existing materials) and 'set_object_material_color' (which only changes color).

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

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

The description implies the tool is for creating or replacing materials, but it does not explicitly state when to use it vs alternatives like 'assign_material' or when to set material properties directly. No when-not-to-use guidance is provided.

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

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

No annotations are provided, so the description carries the full burden. It discloses the non-obvious side effect of purging orphaned mesh/material data, which is good transparency. However, it does not mention other traits like undoability or permissions.

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 extremely concise: one sentence for the purpose and one line for the parameter. Every word is necessary, and there is no 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 simple tool with one parameter and no output schema, the description covers the main action and parameter. However, it does not indicate what the tool returns on success or failure, which is a minor 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 description coverage is 0%, so the description must compensate. It adds the parameter 'name: Object name to delete', which clarifies the parameter's purpose beyond the schema. This is sufficient for a simple tool, though additional format details could be helpful.

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 verb 'delete' and the resource 'object from the scene', and adds the specific detail of purging orphaned mesh/material data. This distinguishes it from other object manipulation tools like rename or duplicate.

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 (to delete objects) but provides no explicit guidance on when not to use it or alternatives. Among siblings, there are many object operations, but no direct comparison or exclusion criteria.

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 explain the diff visualization (desaturation, red highlights) and scaling behavior, but it omits what happens to the composite output—where it is saved, whether it is returned, or any side effects. This is a notable 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 compact and well-structured, with two clear sentences explaining the behavior followed by a concise parameter list. Every sentence adds meaningful information, and there is no redundancy or extraneous detail.

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 input handling and processing behavior, but it lacks information about the output format, destination, or return value. Since there is no output schema and no annotations, this missing context leaves an agent uncertain about what to do with the result.

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 JSON schema provides no parameter descriptions, the tool description explicitly defines each parameter: image_path_a and image_path_b as input paths, threshold as the pixel-difference cutoff (0-255), and tile_size as the longest panel side. This fully compensates for the schema's lack of 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 ('Compare') and resource ('two images') and clearly distinguishes the tool by detailing the 3-panel composite output with A, B, and Diff panels. This makes the tool's purpose unambiguous and differentiates it from siblings like compare_reference_image.

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

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

The description explains how the diff is computed and how images are scaled, but it does not state when to use this tool versus alternatives such as compare_reference_image or get_viewport_screenshot. There is no explicit guidance on appropriate use cases 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?

No annotations are provided, so the description must carry the behavioral burden. It does state that the tool downloads and imports into Blender and returns a success/failure message. However, it does not disclose side effects like modifying the current Blender scene, network requirements, or whether the operation could overwrite existing objects. This is adequate but leaves gaps.

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 well-structured: a one-sentence purpose, a concise parameter block with examples, and a return-value note. It is front-loaded and every line adds value given the schema has no descriptions. The parameter list is slightly long but justified by the lack of schema documentation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

All parameters are covered and the return behavior is stated, which is good for a tool with no output schema. However, the description omits useful context such as where asset_id comes from, whether the import modifies the existing scene, and any dependency on Blender being open. Given no annotations and no output schema, this is a clear 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?

The input schema has 0% description coverage, so the parameter list in the description is the primary source of semantic information. It provides meaningful examples and allowed values for asset_type, resolution, and file_format, including asset-type-specific formats. Asset_id is under-specified (only 'The ID of the asset to download'), but the overall parameter documentation compensates well for the bare 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 a specific verb and resource: 'Download and import a Polyhaven asset into Blender.' This clearly distinguishes the tool from siblings like search_polyhaven_assets (searching) and download_sketchfab_model (different source platform). No ambiguity about what this tool does.

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

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

The description makes it clear this is for Polyhaven assets, which implies when to use it versus Sketchfab or Hunyuan tools. However, it does not explicitly state when to prefer this over search_polyhaven_assets or set_texture, nor does it mention preconditions such as needing an asset_id from a prior search. Usage context is present but only implied.

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 does a good job: it discloses the scaling behavior, the requirement to provide a target size, the return message with import details, and access prerequisites. It does not mention edge cases or failure behavior, but the core side effects are 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 front-loaded with purpose and scaling behavior, then provides a focused parameter list with useful examples. It is slightly repetitive about target_size being required, but no sentence feels unnecessary.

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 (two parameters, no output schema), and the description covers the input semantics, the scaling behavior, prerequisites, and what the return message contains. It could add failure/edge-case behavior, but for its complexity it is 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 description coverage is 0%, but the description compensates well for target_size with units, requirement status, and concrete examples. The uid parameter is only described as 'the unique identifier,' which is thin but sufficient given the tool name and context. This is strong compensation for an otherwise bare 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 starts with a specific action ('Download and import a Sketchfab model by its UID') and clearly identifies the resource and key behavior. This distinguishes it from sibling Sketchfab tools like search_sketchfab_models and get_sketchfab_model_preview, which do not import.

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

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

The description explains that the tool requires a UID and target_size and that the model must be downloadable with proper access rights, which gives some context. However, it never explicitly says when to prefer this tool over alternatives such as search_sketchfab_models or import_generated_asset, so usage guidance is only implied.

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?

Without annotations, the description takes full responsibility. It explains that the linked parameter controls whether the duplicate shares mesh data (instance) or is a full copy, and it specifies the default offset. However, it does not mention what happens when new_name is omitted (auto-assigned) or the return value.

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 with a clear structure: a short introductory sentence followed by a bullet list of parameters. It is front-loaded with the core purpose but could be slightly more compact.

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 4 parameters and no output schema, the description covers parameter behavior well, but it omits any mention of return values or side effects, which would be useful for 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?

The input schema provides only titles and types (0% coverage), but the description adds critical meaning: explains each parameter's role, default values, and the effect of 'linked' (instance vs copy), which significantly aids correct usage.

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 'Duplicate an object.' with a specific verb and resource, and it distinguishes itself from sibling tools like delete_object, rename_object, etc., which have different purposes.

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

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

No explicit guidance on when to use this tool versus alternatives. The parameter descriptions hint at different use cases (e.g., linked=True for instances), but there is no direct comparison or exclusion criteria.

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 burden falls on the description. It does disclose that the tool can run arbitrary Python code, which implies broad power and risk, and the 'step-by-step' warning hints that long or complex executions need caution. However, it does not mention possible destructive scene mutations, absence of undo, execution limits, or error behavior, so the disclosure is only partially complete.

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

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

The description is short and front-loaded with the core purpose. The step-by-step instruction is useful, and the parameter note is simple. The phrase 'step-by-step by breaking it into smaller chunks' is slightly redundant, but overall the text is efficient and easy to scan.

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 arbitrary code execution tool with no annotations and no output schema, the description leaves important gaps: there is no indication of what the tool returns, how failures surface, what Python objects are available in scope, or whether the execution mutates the Blender scene permanently. The minimal description does not give an agent enough context to safely and correctly invoke this powerful tool in varied situations.

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 0%, so the description must compensate. The line 'code: The Python code to execute' adds only marginally more than the schema property title 'Code' and the tool name itself. It does not explain expected code environment details such as whether Blender's bpy module is pre-imported, how results or errors are returned, or whether there are execution limits.

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 and resource: 'Execute arbitrary Python code in Blender.' The word 'arbitrary' signals this is a general-purpose escape hatch, and no sibling tool has the same role, so an agent can immediately distinguish it from the other scene, asset, and model-generation 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?

There is no guidance on when to prefer this tool over the many specialized sibling tools. The instruction to break work into smaller chunks is about how to structure execution, not about choosing this tool over alternatives. The only implied guidance is that 'arbitrary' means it can be used for tasks the other tools don't cover, but that is not made 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 exist, so the description must disclose behavior. It states the action but does not mention side effects (e.g., file overwrite, performance, or permissions). The auto-generation of filepath is noted, but overall transparency is minimal.

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: a one-line summary followed by a clear parameter list. Every sentence is useful, no redundant info, and it is well-structured for quick parsing.

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 action and parameters adequately for a simple tool, but lacks information on return values or error states. Given no output schema, mentioning the output file path would improve 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?

With 0% schema description coverage, the description compensates by explaining each parameter's effect (e.g., name omission exports entire scene, filepath defaults to temp, file_format lists options). This adds significant value beyond the bare 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 exports an object or full scene to a 3D file, with specific formats. It uniquely serves this purpose among siblings, differentiating from save, render, or import 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 lists parameter options but provides no guidance on when to use this tool versus alternatives, nor any prerequisites or conditions (e.g., scene must be loaded). It implies usage for 3D file output but lacks explicit direction.

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 explains the extrusion direction and that negative amount indents, but does not disclose side effects like whether the modification is permanent or if selections are changed.

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 brief and to the point, with a clear purpose statement followed by parameter details. It is front-loaded and efficient, though minor formatting (bold) is present.

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 tool with no output schema, the description covers core functionality. However, it omits context like prerequisites (object must be a mesh) and results (e.g., memory usage, selection state). Adequate but not exhaustive.

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 0% schema description coverage, the description adds significant value by explaining each parameter: name (mesh object), face_indices (comma-separated indices), and amount (extrusion distance, negative for inward, default 0.2). This goes beyond the schema's bare types and 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 explicitly states the action ('Extrude faces') and the method ('along their individual normals'), which clearly distinguishes it from siblings like inset_faces or flip_normals.

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

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

No explicit guidance on when to use this tool versus alternatives such as inset or flip normals. The description implies its purpose but does not provide criteria for selection or exclusion.

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 provided, so the description must disclose behavioral traits. It fails to state that this is a read-only operation, nor does it describe any side effects or requirements like permissions. 'List' implies no mutations, but this is not explicitly stated.

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 extremely concise with no fluff. It front-loads the purpose in the first sentence and then explains the parameter. Every word adds value for a single-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?

For a simple tool with one parameter and no output schema/annotations, the description covers the basic action and parameter. However, it lacks explanation of the output format (what does 'list' return?), leaving some uncertainty for an AI 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 0%, but the description adds meaningful parameter semantics by listing example Blender object types (MESH, CURVE, etc.) and indicating the expected values. This helps the agent understand valid inputs, though the list could be more exhaustive.

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 objects by type, using specific verb 'List' and resource 'objects in the scene'. It is distinguishable from siblings like 'select_objects' or 'get_object_info', but could be more explicit about the output format.

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

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

No explicit guidance on when to use this tool versus alternatives. The description implies usage for filtering objects by type, but does not mention when not to use or provide comparisons to 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?

The description explains the core behavior: it flips normals and the optional parameter defaults to flipping all faces. However, it does not state that the mesh is modified in-place, whether the operation is undoable, or any potential side effects. With no annotations, the description carries full burden and partially meets it.

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

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

The description is extremely concise with two sentences plus a parameter list. Every sentence adds value, and there is no redundant information. It is well-structured and easy to parse.

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 simple operation but lacks important details: no mention of return values (success/failure), error conditions (invalid name, indices out of range), or feedback. In the context of a 2-parameter tool with no output schema, more completeness would be helpful.

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 significant meaning beyond the schema: 'name' is clarified as 'Mesh object name', and 'face_indices' is explained as 'Comma-separated face indices (flips ALL faces if omitted)'. Given 0% schema coverage, the description fully 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 clearly states it flips face normals on a mesh and explains the effect: 'reverses which side is the outside'. This is a specific verb+resource, and it distinguishes this tool from sibling mesh operations like extrude, inset, or triangulate.

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 on when to use this tool versus alternatives, such as recomputing normals or using other mesh editing tools. It does not mention prerequisites (e.g., the mesh must have faces), nor does it provide examples of typical use cases.

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 provided. The description details parameters and their effects (e.g., background removal, resolution trade-off) but does not disclose potential side effects (e.g., file overwriting), permissions, or error conditions. It adds value beyond the schema but lacks full behavioral 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?

Description is efficient: a short purpose statement, a prerequisite sentence, and a bulleted parameter list. No redundant information; all sentences add value. Well-structured for quick parsing.

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 moderate complexity (5 params, 1 required), the description covers purpose, prerequisites, and all parameters. It does not explicitly describe the return behavior (e.g., path to generated file), but generating a .glb file is implied. Slightly more detail on output would improve 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 0%, so the description fully compensates by explaining each parameter (image_path, output_path, foreground_ratio, mc_resolution, no_remove_bg) with meanings, defaults, and optionality. This is highly informative for an agent.

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?

Clearly states the tool generates a 3D mesh (.glb) from a single image using the local TripoSR model. This specific verb+resource combination distinguishes it from sibling tools for other 3D generation methods (e.g., generate_hunyuan3d_model, generate_hyper3d_model_via_images).

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 states a prerequisite: 'You must call load_img_to_3d_model() first.' This provides clear when-to-use guidance. However, it does not mention alternatives or when not to use this tool, though the prerequisite effectively implies the required context.

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 and does well: it discloses asynchronous job submission via job_id, eventual DONE status meaning the model has been imported, error returns, and built-in materials. Could mention scene impact, but key behavior is covered.

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?

Well-structured with front-loaded purpose, a compact Parameters section, and a Returns section. Every sentence adds value 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?

Provides enough context for a generation-and-import workflow: inputs, async behavior, success condition, and error handling. It does not explicitly point to poll_hunyuan_job_status for status checking, but the job_id and DONE status make the flow inferable.

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 0%, but the description fully compensates by defining text_prompt as an optional short English/Chinese description and input_image_url as an optional local/remote URL that accepts None. It also clarifies the relationship between the two: either text, image, or both.

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

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

States a specific action, resource, and method: generate a 3D asset using Hunyuan3D from text, image, or both, and import it into Blender. This distinguishes it from sibling generation tools like generate_hyper3d_model_via_text/images and from standalone import 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?

Clearly indicates when to use the tool (Hunyuan3D generation with auto-import) and how to choose inputs (text, image, or both). It does not explicitly name sibling alternatives or state when-not-to-use, but the context is clear enough for selection.

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 usefully mentions built-in materials, normalized sizing that may require re-scaling, that the asset is imported into Blender, and that a success/failure message is returned. However, it does not disclose whether generation is asynchronous, whether a job ID is produced, or what side effects occur beyond the import, which could be significant for an agent deciding how to proceed.

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 well-structured: a clear one-line purpose, a few meaningful behavioral notes, and a bullet-like parameter section. Every sentence adds value without redundancy, and the most important invocation constraints are placed prominently.

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 parameters and gives relevant behavioral context, but it does not explain how an agent can determine the current Hyper3D Rodin mode, what image requirements exist, or what the success/failure message contains. Since there is no output schema and sibling polling tools exist, the lack of async/job details leaves some ambiguity 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?

The input schema has 0% description coverage, so the description fully compensates. It explains that input_image_paths must be absolute paths, that single values must still be wrapped in a list, that the three-element bbox_condition controls Length/Width/Height ratio, and that the two input parameters are mutually exclusive based on the current mode. This is strong parameter-level guidance.

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: 'Generate 3D asset using Hyper3D by giving images' and 'import the generated asset into Blender.' It clearly distinguishes itself from the sibling text-based generation tool by emphasizing image input, so an agent can identify when this tool is relevant.

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 concrete guidance on parameter selection: input_image_paths is for MAIN_SITE mode, input_image_urls is for FAL_AI mode, and exactly one must be provided. It does not explicitly name alternative generation tools or state when not to use this tool, but the image-vs-text distinction is clear enough to infer the primary use case.

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 well by disclosing that the tool imports the asset into Blender, that materials are built-in, that the model size is normalized and may need rescaling, and that it returns a success/failure message. It does not mention whether generation is asynchronous, whether it blocks, or how the imported object can be referenced afterward, but it provides substantial useful behavior context.

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 well-structured: purpose and side effect first, then relevant model characteristics, then parameter details, then return value. Every sentence adds information, and there is no redundant restatement of schema fields or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter tool with no annotations and no output schema, the description is nearly complete. It covers input semantics, generated asset characteristics, the Blender import side effect, and the return message. Minor gaps are lack of clarification about asynchronous behavior and how the agent can locate or reference the imported asset after success.

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 0%, so the description's parameter notes are essential. It explains that text_prompt must be a short English description, and that bbox_condition is optional, must be a list of floats of length 3, and controls the Length/Width/Height ratio. This adds meaningful semantic detail beyond the raw schema, though ranges or a more precise ratio explanation would strengthen it further.

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 ('Generate'), the specific service (Hyper3D), the input mode (description/text), and the side effect (import into Blender). It also adds useful purpose details: built-in materials and normalized size. This clearly distinguishes it from image-based generation like generate_hyper3d_model_via_images, even though that sibling is not named.

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 have a textual description of a 3D asset and want it generated and imported into Blender. However, it does not explicitly contrast with generate_hyper3d_model_via_images, import_generated_asset, or the other generation tools. No when-not-to-use guidance or alternative selection criteria are provided.

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

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

No annotations provided, but the description fully discloses the tool's behavior: it reports two boolean statuses. No side effects or hidden traits.

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?

Single sentence with no wasted words. Perfectly concise.

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?

Tool is simple with no parameters, no output schema. Description adequately conveys purpose and output.

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?

No parameters exist, so the description adds no parameter info, but schema coverage is 100% and baseline 4 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 reports whether Blender is running and whether the MCP addon is reachable. This is specific and distinguishes it from sibling tools that perform actions or modify objects.

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: it's a status check. No explicit when-not or alternatives, but the context is clear for a simple status 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?

The description discloses that it is a read operation returning different control point data per curve type and details parameters. However, it does not mention error conditions (e.g., if the object is not a curve) or performance considerations.

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 very concise with a clear structure: main purpose in the first sentence, then return types, then parameter list. No extraneous 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?

The description covers the tool's purpose, parameters, and return types per curve type. Without an output schema, it provides valuable context. It could be more complete by mentioning that the object must exist and be a curve.

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 0% schema description coverage, the description fully compensates by explaining each parameter: name as 'Curve object name' and spline_index as 'Which spline within the curve (default 0)', adding meaning 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 clearly states the tool reads control points of curve objects (BEZIER, POLY, NURBS) and specifies the returned data per type, differentiating it from sibling tools like get_vertex_positions or set_control_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?

The description does not provide explicit guidance on when to use this tool versus alternatives. While the purpose is clear, there is no mention of exclusions or contexts where other tools might 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?

The description identifies the tool as read-only ('Read edge data') and mentions the safety cap (max_edges) with default. Without annotations, it adequately discloses the read nature and safety limit, though no further behavioral details are given.

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 extremely concise: one sentence defining purpose, one listing returned data, then a clear parameter list. Every word adds value, and key info 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?

The description covers purpose, parameters, and returned data fields. Without an output schema, it could provide more detail on the structure of 'vertex pair' (e.g., indices or coordinates), but it is sufficiently complete for a read operation on edges.

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 extensive meaning to all parameters: name (mesh object), indices (comma-separated, returns all if omitted), max_edges (safety cap, default 5000). This fully compensates for the 0% schema description 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 reads edge data from a mesh and lists the specific fields (vertex pair, sharp flag, etc.), distinguishing it from sibling tools like get_faces or get_vertex_positions.

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 purpose is clear, but no explicit guidance on when to use it versus alternatives (e.g., get_mesh_stats) or when not to use it. The description lacks contextual usage cues.

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 cover behavioral traits. It mentions a safety cap for max_faces and that it returns data (read-only), which is adequate but does not discuss edge cases or permissions.

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: one sentence for purpose, bullet points for parameters, and a final sentence on return value. No wasted words.

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 return JSON structure (vertex_indices, normal, etc.) despite no output schema. All parameters are covered, making it complete for a read tool. However, it could mention if the tool modifies the mesh or requires any permissions.

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 0%, so the description compensates by explaining each parameter: 'name' (mesh object), 'indices' (comma-separated, returns all if omitted), 'world_space' (default True), and 'max_faces' (safety cap, default 2000). This adds significant meaning 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 starts with a clear verb and resource: 'Read face data from a mesh object.' This distinguishes it from sibling tools like get_edges or get_vertex_positions.

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 does not provide guidance on when to use this tool versus alternatives such as get_edges or get_mesh_stats. It lacks explicit context for selection.

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 does disclose that the tool returns a message about Hunyuan3D feature availability, but it does not explain the message format, the meaning of 'key type,' or any side effects/auth requirements. The instruction to 'silently remember it' is ambiguous.

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

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

The description is short and the core purpose is front-loaded. However, the first two sentences are somewhat redundant ('Check if enabled' vs 'Returns message indicating whether available'), and the final sentence about not emphasizing the key type is unclear and may confuse rather than help.

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 status check with no output schema, the description gives enough to invoke the tool correctly: it checks Hunyuan3D integration and returns an availability message. The incomplete area is the vague 'key type' mention, but the low complexity keeps the gap minor.

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 tool has zero parameters and the empty input schema covers 100% of the parameter space. With no params, the description does not need to add param semantics 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 a specific verb and resource: 'Check if Hunyuan3D integration is enabled in Blender.' It also specifies the return value as a message about feature availability. This clearly differentiates it from sibling status tools such as get_hyper3d_status and get_sketchfab_status by naming the Hunyuan3D integration.

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 implied from the description: call it when you need to know whether Hunyuan3D is enabled. However, it does not explicitly mention alternatives or provide when-not-to-use guidance, relying on sibling names to distinguish which status tool to pick.

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 carry the behavioral burden. It does disclose that the tool returns a status message and includes an unusual instruction about not emphasizing the 'key type' and silently remembering it. However, 'key type' is undefined, and the description does not explicitly confirm the operation is read-only or mention any prerequisites or side effects.

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 first two sentences are concise and front-loaded with the core purpose and return behavior. The third sentence about the 'key type' is short but confusing, contains a typo ('sliently'), and is not clearly connected to the rest of the description, preventing a higher score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter status tool with no output schema, the description gives the essential high-level behavior: it checks integration availability and returns a message. Still, the meaning of 'key type' is unexplained and no guidance is offered about what the agent should do with that remembered information, leaving some context incomplete.

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 tool has zero parameters, so the schema already provides complete parameter information. The description adds no parameter details, but none are needed; this matches the baseline of 4 for tools with no 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 a specific action ('Check if Hyper3D Rodin integration is enabled'), a specific resource ('in Blender'), and the expected result ('Returns a message indicating whether Hyper3D Rodin features are available'). This clearly distinguishes it from sibling tools like get_sketchfab_status and get_hunyuan3d_status.

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 reasonably clear from the tool name and the phrase 'Hyper3D Rodin integration', and sibling tools imply alternatives for other services. However, there is no explicit guidance about when to prefer this tool over siblings or exclusions, so the usage guidance remains mostly 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?

No annotations are provided, so the description must disclose behavioral traits. It only states 'Return...' without clarifying whether the tool is read-only, has side effects, requires permissions, or any other behavioral context. For a stats-gathering tool, this is insufficient.

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 very brief (two sentences plus a parameter list) and front-loaded with the main purpose. Every sentence is functional, but the structure could be improved by grouping related information (e.g., a separate usage note).

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 one parameter, no output schema, and no annotations. The description leaves 'detailed topology statistics' vague; the agent cannot infer the return format (e.g., counts, dictionaries) or understand edge cases. Given the lack of output schema, more context is needed.

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 0%, so the description must compensate. It notes that 'name' is a 'Mesh object name,' adding a type constraint beyond the schema's 'Name' title. However, this is minimal; the description does not explain the expected format or hint at validation rules.

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 returns 'detailed topology statistics for a mesh object,' using a specific verb-resource pair. This differentiates it from sibling tools that return specific mesh components (e.g., get_vertex_positions, get_faces, get_edges).

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 on when to use this tool versus alternatives. For example, it does not explain that for individual component queries one should use other sibling tools, nor does it mention prerequisites or typical use cases.

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 is the only source for behavior. 'Get' implies a read-only operation, which is helpful, but there is no disclosure about errors, missing objects, or what 'detailed information' includes. This is adequate but leaves room for surprise.

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

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

The description is short and front-loaded with the core purpose, and the parameter note earns its place given the schema's lack of documentation. The parameter list is slightly redundant with the schema, but it does not waste words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool, the description provides the essential calling information and a clear purpose. However, with no output schema, it does not clarify what 'detailed information' returns, leaving an agent without expectations about the response contents.

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 provides only the parameter name with no description (0% coverage), but the tool description adds a functional definition: 'object_name: The name of the object to get information about'. This meaningfully clarifies the sole parameter, though it does not address exact-name matching or behavior for nonexistent objects.

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 verb 'Get', the resource 'detailed information', and the specific scope 'specific object in the Blender scene'. It distinguishes this from scene-level tools like get_scene_info, though it does not explicitly name or contrast siblings.

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 use this tool versus alternatives. Sibling tools like execute_blender_code or get_scene_info could overlap, but the description provides no exclusions or contextual selection criteria.

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 accurately signals a read-only operation ('Get a list'), but it does not disclose return format, network dependency, or error behavior. For a simple list operation this is adequate but not rich.

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

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

The description is compact, with the core action front-loaded and a structured parameter list. However, the parameter list partially duplicates the schema, even though it adds the allowed values.

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 one optional parameter and no output schema, the description covers the main semantic need: what categories are for and the allowed asset types. It lacks an explicit return-format note, but 'list of categories' implies an array of names. Minor gap: no statement about the default when asset_type is omitted, though the schema covers this.

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's Parameters section provides the accepted values for asset_type (hdris, textures, models, all), which the schema omits. This adds critical meaning beyond the schema's bare string type and default.

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 'Get a list of categories for a specific asset type on Polyhaven' – a precise verb+resource. This clearly distinguishes it from siblings like search_polyhaven_assets, download_polyhaven_asset, and get_polyhaven_status, which concern asset search, download, and service status.

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 on when to use this tool versus alternatives. It does not specify a workflow (e.g., fetch categories before searching assets) or any exclusions. The parameter list is not usage 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 states the tool 'returns a message' about feature availability, which implies a read-only status query, but it does not disclose whether it queries Blender state or makes network calls, nor possible errors. The non-mutating nature is inferred from 'Check' rather than stated.

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 short sentences with no filler. The primary action is front-loaded, and the return behavior is separated in the 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?

For a zero-parameter status check, the description provides the essential purpose and return information. It lacks an explicit statement of the message format or error behavior, and no output schema compensates, so it is not fully specified.

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 tool has zero parameters, so there is nothing to document. The baseline of 4 applies because schema coverage is complete and the description correctly says nothing about 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 uses the specific verb 'Check' with the resource 'PolyHaven integration in Blender', making the operation immediately clear. It names PolyHaven, distinguishing it from sibling status tools like get_sketchfab_status and get_hyper3d_status.

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

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

The description implies the tool is a capability check but gives no explicit guidance on when to invoke it or when to prefer a sibling status tool. There is no mention of using it before PolyHaven asset operations or an alternative. This is acceptable but relies on 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, the description must carry the full behavioral burden. 'Get' weakly implies a read-only operation, but it does not disclose what 'detailed information' contains, whether it errors under certain conditions, or whether it requires a valid Blender context. It provides very little beyond the tool name itself.

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 clear sentence with no filler or redundancy. The key components—verb, resource, and scope—are all present and front-loaded. Nothing extra needs to be trimmed.

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 zero-parameter tool with no annotations and no output schema, so the description is the only source of behavioral and return-value information. Saying only 'detailed information' leaves the agent uncertain about what fields or values will be returned, making the definition incomplete for a tool that provides scene info.

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, so there are no parameters to document. With a parameter count of 0, the baseline of 4 is appropriate because no parameter meaning needs to be added by the 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 clear verb ('Get') and resource ('current Blender scene'), making the basic purpose understandable. However, 'detailed information' is vague about exactly what scene data is included, and it does not explicitly differentiate from get_object_info, though scene vs. object is an implied 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 context is implied: use this when you need detailed scene-level information. But there is no guidance on when not to use it, no mention of alternatives like get_object_info, and no prerequisites such as requiring an open Blender scene or active session.

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

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

No annotations exist, so the description carries the full burden. It does disclose the return value ('thumbnail as an Image') and implies a non-destructive preview via 'visually confirm'. However, it does not explicitly state that this is a read-only operation, whether it makes a network request, or how failures are surfaced. For a simple preview tool this is acceptable but not thorough.

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 well-organized: one sentence for purpose, one for usage, one clear parameter line, and one for the return value. Every sentence earns its place with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has one required parameter, no output schema, and simple semantics. The description covers the purpose, the parameter source, and the return type. Missing details like error cases, licensing implications, or the fact that no model data is fetched are minor for a preview thumbnail tool, but would make it even stronger.

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 0%, so the description must compensate. It adds crucial meaning to 'uid' by defining it as the unique Sketchfab model identifier and specifying its source (search_sketchfab_models). It stops short of giving format, example, or validation constraints, but for a single self-explanatory parameter this is solid.

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 ('Get') and resource ('preview thumbnail of a Sketchfab model'), and identifies the input (UID). It clearly distinguishes its role from sibling tools like download_sketchfab_model by framing it as visual confirmation before downloading.

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 instructs when to use the tool: 'Use this to visually confirm a model before downloading.' It also tells agents where the UID comes from (search_sketchfab_models), which is contextual guidance. It does not explicitly name alternatives or when-not-to-use cases, so it misses the top of the scale.

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 transparency burden. It does communicate a non-mutating status check and a message return, but it does not state whether the check is local-only or queries Sketchfab, nor any side-effect or error behavior. This is adequate but not rich.

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 short sentences, front-loaded with the purpose and followed by the return behavior. No filler or duplication beyond a mild redundancy between 'enabled' and 'available'.

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 status check with no output schema, the description covers the essential aspects: what is checked and what the caller receives. It could be more specific about the exact structure/form of the returned message, but nothing critical is missing.

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

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

The tool has zero parameters, so there is nothing for the description to document. The schema already covers 100% of the empty parameter surface, earning the zero-parameter baseline of 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 uses a specific verb ('Check') and resource ('Sketchfab integration in Blender'), and clarifies the outcome ('Returns a message indicating whether Sketchfab features are available'). It is unambiguous and distinguishable from sibling status tools for other integrations by naming the exact integration.

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: an agent would call this when it needs to know whether Sketchfab is available in Blender. However, there is no explicit guidance about when not to use it, nor any comparison with sibling status tools like get_hyper3d_status or get_polyhaven_status.

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 mentions 'Read' implying read-only, and includes a safety cap (max_verts), but does not explicitly state that no changes are made or other behavioral traits like permissions.

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: a short sentence explaining purpose, followed by parameter explanations in a list, and a return format note. No extraneous information; front-loads the key 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 the tool has 4 parameters, no output schema, and no annotations, the description covers all parameters and the return format. It lacks error cases or coordinate system details, but overall is quite complete for a read operation.

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 description coverage is 0%, and the description adds meaningful explanations for all four parameters: name, indices (comma-separated), world_space (default true), and max_verts (safety cap). This compensates for the missing 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 clearly states 'Read vertex positions from a mesh object,' specifying the action (read) and resource (mesh object). It distinguishes from siblings like set_vertex_positions by indicating a read operation.

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 indicates the tool is for reading vertex positions, but does not provide explicit guidance on when to use it vs alternatives or when not to use it. No exclusions or context are given.

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 basic behavior (captures viewport, returns an Image) but does not mention whether the scene is modified, what image format is used, or any viewport/display requirements. For a screenshot tool this is adequate but not deeply 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 short and well-structured: a one-sentence purpose, a compact parameter definition, and a return note. Every part earns its place with no 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?

For a simple tool with one optional parameter and no output schema, the description is nearly complete: it states what it captures, how max_size behaves, and that it returns an Image. Minor missing details like image format or whether overlays are included keep it from a 5.

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 0%, so the description must compensate for the single parameter. It does so effectively by explaining that max_size is 'Maximum size in pixels for the largest dimension' and noting the default. This adds meaning beyond the bare schema property.

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, specific verb and resource: 'Capture a screenshot of the current Blender 3D viewport.' This unambiguously identifies the tool's function and distinguishes it from the generation/status/polyhaven/sketchfab siblings.

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 by the resource: use this when an image of the current Blender viewport is needed. However, there is no explicit guidance about when not to use it or how it relates to alternatives, though no sibling tool appears to provide the same screenshot capability.

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. The description only states the action and supported formats, omitting behavioral details like whether the file must exist, side effects on the scene, or error handling. The description does not compensate for the lack of 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 very concise: two sentences plus a list of formats. No wasted words, 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 simple import tool with one parameter, the description covers the essential points: action, supported formats, and parameter meaning. It does not describe return values, but no output schema exists. Adequate for the 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 0%, so the description must compensate. It describes the parameter 'filepath' as 'Absolute path to the file to import', adding meaning beyond the schema. However, it lacks format validation details or examples.

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 verb 'Import' and the resource '3D file into the current Blender scene', and lists supported formats. It is distinct from export but does not explicitly differentiate from other import tools like import_generated_asset, though the format list helps.

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 on when to use this tool versus alternatives (e.g., import_generated_asset, load_blend). No prerequisites or when-not-to-use context provided.

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

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

With no annotations, the description does the work: it states the prerequisite ('after the generation task is completed'), the mode-dependent parameter behavior, and that it returns a success/failure indicator. However, it does not disclose side effects on the scene, such as whether importing with an existing name replaces or adds an object, nor does it describe error 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 tightly structured: one sentence of purpose, a bullet-style parameter list, a prominent mutual-exclusivity warning, and a return statement. Every line adds needed operational information with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple three-parameter tool with no output schema, it covers the prerequisite, parameter meaning, mode-based selection, and return status. Minor gaps remain: the exact return type is not specified ('Return if...' could be read as 'whether' versus 'the asset'), and behavior when neither/both ID fields are supplied is only partially covered by the instruction.

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 has 0% coverage for parameter descriptions, but the tool description fully documents all three parameters: name as the scene object name, task_uuid for MAIN_SITE mode, request_id for FAL_AI mode, plus the critical rule that exactly one of the two IDs should be provided based on 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 opens with a specific action and target: 'Import the asset generated by Hyper3D Rodin after the generation task is completed.' This clearly distinguishes the tool from the sibling import_generated_asset_hunyuan and from polling/status tools, so an agent knows exactly what this tool is for.

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 usage context: call after the generation task is completed, and choose task_uuid or request_id according to the Hyper3D Rodin mode (MAIN_SITE vs FAL_AI). It stops short of explicitly contrasting with alternative tools such as import_generated_asset_hunyuan, so it does not quite earn 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 full transparency burden. It discloses the timing prerequisite and the return behavior ('Return if the asset has been imported successfully'), and the mutation is implied by 'import'. However, it does not mention failure modes, whether an existing object with the same name is overwritten, or any side effects on the current scene.

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

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

The description is short, front-loaded with the main action, and uses a clear parameter list and return statement. Every sentence contributes; the only minor issue is slight redundancy between 'after the generation task is completed' and the parameter note about the generate step, but overall it is 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?

For a simple two-parameter tool with no output schema or annotations, the description covers the core purpose, parameters, timing, and return value. It is missing explicit edge-case behavior (e.g., invalid zip_file_url, generation not finished, duplicate names) and does not clarify the exact return format, leaving moderate gaps 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 0%, so the description must provide meaning for both parameters, and it does: 'name' is clarified as the scene object name, and 'zip_file_url' is explicitly tied to the generate model step. This adds practical semantics beyond the bare schema titles, though the URL format could be more precise.

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 ('Import') and names the exact resource ('asset generated by Hunyuan3D after the generation task is completed'), which clearly distinguishes it from other generation/import tools. However, it does not explicitly contrast itself with the sibling 'import_generated_asset', leaving some ambiguity about which import variant to use.

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 a clear precondition ('after the generation task is completed') and references the 'generate model step' for obtaining zip_file_url, which gives an agent a concrete workflow. It does not explicitly name alternatives or when not to use this tool, but for a focused import tool this is adequate context.

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 explains the effect but omits critical behavioral details: whether the operation is destructive, if it requires edit mode, what happens to selection, and error handling for invalid indices. The 'depth' parameter explanation is helpful but incomplete.

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?

Description is a single focused sentence followed by a clean parameter list. Every sentence adds value; no redundant or vague phrasing.

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 moderate complexity, the description lacks details on return value, side effects, and error conditions. It assumes familiarity with Blender's inset operation, leaving gaps for an AI 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 coverage is 0%, so description must compensate. It adds default values and explanations for all parameters, notably clarifying 'depth' (0=flat, positive=raised) and face_indices format. However, it does not specify the indexing scheme (0-based) or expected format beyond comma-separation.

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 action ('Inset faces') and the outcome ('creating a border ring of new polygons inside each face'). It distinguishes itself from siblings like 'extrude_faces' and 'subdivide_mesh' by specifying a unique geometric operation.

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 provided on when to use this tool versus alternatives (e.g., extrude_faces, subdivide_mesh). There is no mention of prerequisites, mesh state, or scenarios where inset is inappropriate.

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 provided, so description must disclose behavior. It does not state whether original objects are removed, if the operation is destructive, or what happens to materials/transforms.

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

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

The description is short and includes parameter details, but it could be more concise by integrating parameter info into the main sentence. It is not overly verbose.

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 a modeling context, the description lacks details about the result (e.g., is a new object created? old ones deleted?) and does not address potential errors or naming conflicts.

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 0% schema coverage, the description adds meaning by explaining that 'names' is a comma-separated list and 'result_name' defaults to the first object's name. This compensates for the sparse 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 verb 'Join' and the resource 'multiple mesh objects into one,' which is specific and distinguishes from siblings like 'separate_mesh' and 'merge_vertices.'

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 on when to use this tool versus alternatives like 'boolean_operation' or 'parent_object.' No prerequisites or exclusions mentioned.

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 transparently discloses the destructive action (replacing the scene) and the loss of unsaved changes. However, it does not mention file existence requirements or loading dependencies, but for a simple load operation this is acceptable.

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: two sentences plus a parameter list. It front-loads the purpose and warning, with no unnecessary words.

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 one-parameter tool with no output schema, the description covers essential behavior (opening a file, replacing scene, warning about unsaved changes). It could mention that Blender must be running, but that is likely implicit. Overall, it is complete enough.

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 has 0% coverage (no descriptions), so the description must compensate. It provides 'filepath: Absolute path to the .blend file to open', adding meaning (absolute path, file type) beyond the schema's title and type.

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 'Open a .blend file, replacing the current Blender scene.' It specifies the verb 'open' and the resource '.blend file', and distinguishes from siblings like import_file by noting the replacement behavior.

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 includes a warning about unsaved changes being lost and advises to save first, but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternative tools like import_file for adding assets without replacement.

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 behavioral disclosure, and it does so thoroughly: it mentions server persistence, VRAM deallocation on unload, first-run weight downloads, model_dir environment fallback, timeout semantics, and where console output goes. These are exactly the side effects an agent needs to predict before calling.

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 main action is front-loaded in one sentence, the lifecycle/VRAM caveat follows immediately, and the parameter explanations are compact and scannable. Every sentence contributes operational value with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple start-server tool with no annotations and no output schema, the description covers purpose, parameters, side effects, and cleanup. It could clarify what happens when called while the server is already running and what the function returns, but those are minor given the tool's clear role.

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 only provides parameter names, types, and defaults (0% schema description coverage). The description adds real meaning: model_dir points to TripoSR weights and falls back to an environment variable, timeout controls how long to wait for startup, and the first run downloads weights. This fully compensates for the bare 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 a specific verb and resource: 'Start the local image-to-3D inference server (TripoSR).' It clarifies that 'load' means starting a server, not directly loading an image, and it references the paired unload sibling, which helps distinguish it from the many other image-to-3D 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?

It gives clear lifecycle context: the server stays running until unload_img_to_3d_model() is called, and the VRAM note tells the agent to invoke it only when needed. It does not explicitly compare against sibling generation tools like generate_hyper3d_model_via_images or generate_3d_from_image, so it stops short of full 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?

No annotations exist, so the description carries full responsibility. It mentions the prerequisite of a Principled BSDF node, but does not disclose whether the tool replaces existing textures, what happens to image file errors, or any side effects on the material graph. This leaves significant behavioral unknowns.

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 with a one-line summary followed by a parameter list. No extraneous information, though the parameter descriptions could be integrated more tightly into the narrative. Still, it is efficient for an agent to parse.

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 tool's main action and parameter meanings, and notes the node prerequisite. However, it lacks details on return values, error conditions, and whether the operation is additive or replaces existing textures, leaving gaps in understanding for a complex operation.

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 0% description coverage, but the tool description compensates by explaining each parameter's role, providing allowed values for texture_slot, and clarifying the requirement for material_name. This adds substantial meaning beyond the schema's bare titles and 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 clearly states it loads an image and wires it into a material's texture slot, using specific verbs and resources. However, it does not explicitly distinguish from sibling tools like `set_texture`, which could be a similar operation, leaving some ambiguity.

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 on when to use this tool versus alternatives. The description lists parameters but does not provide context for appropriate usage, such as when a texture needs to be newly loaded versus reassigned, or prerequisites beyond the node requirement.

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?

Describes visual effect (hard crease vs smooth blend) but does not disclose whether operation is additive or overwrites existing sharpness, or any side effects on modifiers. With no annotations, description carries full burden, adequate but not thorough.

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?

Concise one-paragraph structure with clear separation of purpose and parameter descriptions. No unnecessary words, every sentence adds value.

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?

Covers purpose, parameter details, and visual output. Missing notes on prerequisites (auto-smooth enabled, Edge Split modifier) and reversibility. Given complexity, adequate but not exhaustive.

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?

Adds significant value beyond schema: explains edge_indices format ('comma-separated or all') and sharp parameter meaning (true=hard, false=soft). Schema coverage was 0%, so description compensates fully.

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: marking edges as sharp or soft, and explicitly links it to auto-smooth and Edge Split modifier. It distinguishes from siblings like set_edge_crease by focusing on smooth shading behavior.

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: sharp edges create creases under smooth shading, soft edges blend. Implicitly tells when to use, but does not explicitly mention when not to use or alternatives.

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 provided, so description must disclose behavior fully. It states Euclidean distance but omits details like units, coordinate space, error handling (e.g., missing objects), and behavior for identical objects.

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?

Description is extremely concise: two sentences and a compact parameter list. Every word adds value, with no 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 tool with no output schema, description should explain return value. Missing details like return type (number?), units, and error behavior. Adequate for simple tool but incomplete for full agent understanding.

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 0%, so description carries full burden. It adds brief but essential definitions: 'First object name' and 'Second object name'. While simple, it provides meaning beyond the schema's type and title.

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 verb 'measure' and resource 'Euclidean distance between the origins of two objects'. It is distinct from sibling tools, none of which measure distance.

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?

Description does not provide explicit guidance on when to use this tool versus alternatives. Context implies measurement, but no exclusions or comparisons to sibling tools like get_object_info.

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 disclose behavioral traits. It explains the core merging behavior and practical context, but does not mention potential side effects (e.g., irreversibility, object modification, or any prerequisites). A higher score would require more explicit details about the operation's impact.

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 extremely concise: three sentences plus a parameter list. The first sentence immediately states the purpose, followed by a helpful analogy and use cases. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.