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scatter

Place multiple copies of an object randomly on surfaces with control over area, distance, slope, alignment, scale, and rotation; get counts and rejection reasons.

Instructions

Place count copies of an object at random on top of surface objects, standing on the surface: trees, rocks, crates, grass, sprinkles. source may be a list: each copy takes one at random and weights sets the odds. area [x0,y0,x1,y1] limits where (default: the surface bounds); min_distance keeps copies apart; max_slope_deg skips steep ground; align_to_normal tilts them with the ground; scale_range [min,max] and yaw_random vary them. The same seed gives the same result. linked=true shares one mesh (cheap). The answer counts the copies per source and why tries were rejected (too_close, not_on_surface, too_steep). For one object at a chosen spot use place_on.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaNo
nameNo
seedNo
countYes
linkedNo
sourceYes
surfaceYes
weightsNo
yaw_randomNo
scale_rangeNo
min_distanceNo
max_slope_degNo
align_to_normalNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.8/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses randomization behavior, seed reproducibility, linked mesh sharing, default area bounds, and rejection reasons (too_close, not_on_surface, too_steep). This gives the agent strong insight into how placement behaves and what the result reports.

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

Conciseness5/5

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

The description is dense but well-organized, front-loading the core purpose before parameter details and the alternative tool. Every sentence adds useful semantic or behavioral information for a 13-parameter tool, with no filler.

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

Completeness5/5

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

Given a complex 13-parameter tool with no annotations and no output schema, the description is unusually complete. It covers purpose, parameter meanings, return summary, seed behavior, and the relevant alternative tool. The only minor gap is the undescribed name parameter.

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

Parameters5/5

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, and it does for almost every parameter. It defines area format and default, min_distance, max_slope_deg, align_to_normal, scale_range, yaw_random, source list behavior, weights, seed, linked, and implied count. Only the optional name parameter is not explicitly described, which is a minor omission.

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

Purpose5/5

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

States a specific verb ('Place') and resource ('copies of an object at random on top of surface objects'), with concrete examples. It clearly distinguishes random scattering from the single-object placement handled by place_on. An agent can identify the tool's purpose without opening the schema.

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

Usage Guidelines4/5

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

Explicitly routes the single-object-at-a-chosen-spot case to place_on, and the examples imply use for random scattering. It does not explicitly contrast with deterministic placement tools like array or radial_array, though the random-placement purpose makes the distinction inferable. Clear context, but not a full when/when-not comparison.

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