Fits a least-squares linear regression to paired x and y data, returning slope, intercept, correlation, R-squared, and the equation for trend analysis.
Fit y = slope·x + intercept by least squares. Returns slope, intercept, r, r_squared and the equation string.
| Name | Required | Description | Default |
|---|---|---|---|
| x | Yes | ||
| y | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden; it does disclose the estimation method (least squares) and the named return fields, implying a pure computation with no side effects. It does not state input preconditions (equal-length arrays, minimum sample size) or error behavior on degenerate input, which are the realistic failure modes.
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 waste, front-loading the model being fitted before the return values. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so restating return fields is not strictly needed, and the core operation is well conveyed for a simple two-parameter tool. The remaining gap is operational: no array-length or sample-size constraints, which is exactly what an agent needs to avoid a failed call.
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% and neither parameter is documented, so the description must compensate. The equation 'y = slope·x + intercept' implies x is the independent variable and y the dependent one, which adds real meaning, but nothing is said about matching lengths or numeric-only contents.
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 gives an explicit verb and model ('Fit y = slope·x + intercept by least squares') plus the returned quantities, so an agent knows exactly what computation happens. It does not mention the sibling analysis_correlation or state how this differs from it, leaving differentiation implicit.
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 when-to-use guidance, no mention of alternatives such as analysis_correlation for association-only questions, and no prerequisites. The only usage signal is the implicit math, which the agent must infer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.