Calculates the Pearson correlation coefficient between two numeric datasets, returning r, r², direction, and strength to assess linear relationships.
Pearson correlation coefficient (r, r², direction, strength).
| 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?
No annotations are provided, so the description carries the full burden. It discloses the output metrics but says nothing about preconditions (equal-length arrays, minimum sample size, numeric-only input), failure behavior on mismatched lengths, or that the operation is a stateless read with no 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?
It is a single short parenthetical fragment with zero padding and the key term front-loaded, which is good. But the brevity crosses into under-specification rather than efficiency, since nothing about inputs or usage survives the trim.
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 return values need not be described in depth (the parenthetical partly duplicates it anyway). Still, for a two-required-parameter statistical tool with no annotations and 0% parameter coverage, the definition leaves out the constraints and usage context an agent needs to call it correctly.
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%: both required parameters are bare arrays titled only 'X' and 'Y' with no element semantics. The description does not compensate by explaining that x and y are paired numeric samples that must be the same length and non-constant, leaving real ambiguity for the caller.
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 names the specific statistic (Pearson correlation) and lists the returned metrics (r, r², direction, strength), so the resource is identifiable. However it is a noun fragment with no verb, and it does not distinguish itself from siblings such as analysis_linear_regression or analysis_stats, which overlap in scope.
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 at all: nothing says to pick this over analysis_linear_regression when you want relationship strength rather than a predictive model, nor over analysis_stats for a general summary. The agent must infer the choice from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.