Compute a Bezier curve from control points, calculate its arc length, and evaluate points at specified t values when needed.
Compute a Bezier curve from control points with arc length and optional evaluation.
| Name | Required | Description | Default |
|---|---|---|---|
| steps | No | Number of curve samples (default 50, max 1000). | |
| eval_at | No | t value(s) in [0,1] to evaluate at. Number or array of numbers. | |
| control_points | Yes | Array of [x, y] control points (at least 2). |
Changes observed during successful MCP inspections.
v0.1.11Does 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 the outputs computed (arc length, optional point evaluation), which is useful, but says nothing about the return shape, default behavior, or edge cases. For a pure computational tool with no side effects, this is adequate but thin.
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 front-loaded sentence with no filler. Every clause (control points, arc length, optional evaluation) conveys a distinct part of the tool's behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-annotation, no-output-schema computation tool, the description should clarify what the returned structure looks like (sampled points, arc length scalar, evaluated points). It hints at outputs but leaves the agent guessing at the response shape.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all three parameters (control_points, steps, eval_at) are already documented in the schema. The description reiterates control points and eval but adds no format, ordering, or constraint detail beyond the schema. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb+resource ('Compute a Bezier curve from control points'), clearly distinguishing this from a generic math call. It does not explicitly differentiate from close siblings like bezier_clip or catmull_rom, but the resource is specific enough to identify the 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 on when to use this versus the sibling bezier_clip, spline_interpolate, or catmull_rom. The agent must infer from the name alone that this generates samples of a Bezier curve.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.