cubic_spline_interpolate

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 'smooth curve fitting' which implies continuity and differentiability, but doesn't disclose critical behavioral traits like error handling, performance characteristics, mathematical assumptions (e.g., sorted x_points), or what happens with invalid inputs. For a numerical tool with zero annotation coverage, 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 extremely concise—a single sentence with domain/category in parentheses. Every word earns its place: 'Perform' (action), 'cubic spline interpolation' (method), 'for smooth curve fitting' (purpose), and context in parentheses. There's no fluff or redundancy.

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

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

Given the complexity of cubic spline interpolation (a mathematical operation with multiple parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It doesn't explain what the tool returns, error conditions, or mathematical details. The conciseness comes at the cost of leaving too much undefined for effective use.

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

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

The description adds zero information about parameters beyond what's in the schema. With 0% schema description coverage and three required parameters (x_points, y_points, x), the description doesn't explain what these arrays represent, their relationships, constraints (e.g., equal lengths, monotonic x), or the interpretation of the output. This leaves parameters completely undocumented.

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 purpose: 'Perform cubic spline interpolation for smooth curve fitting' with domain and category context. It specifies the verb ('perform'), resource ('cubic spline interpolation'), and objective ('smooth curve fitting'), though it doesn't explicitly differentiate from sibling interpolation tools like 'linear_interpolate' or 'lagrange_interpolate'.

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. While the domain/category hints at numerical interpolation contexts, there are no explicit instructions about when cubic spline interpolation is preferred over other methods (e.g., linear or Lagrange interpolation), nor any prerequisites or limitations mentioned.

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