Covariance Matrix: Computes the population covariance matrix over the columns of a numeric matrix 🆓 5 free trial calls per signed wallet
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "x402-covariance-matrix"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| data | No | ||
| matrix | No |
Changes observed during successful MCP inspections.
Input schema / properties / data / descriptionRemoved value: -"Data to process"Input schema / properties / matrix / descriptionRemoved value: -"Matrix to process"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 says nothing about whether the operation is read-only, what the output shape is, what happens on malformed input, or the input format expected for a string-typed matrix. Only the trial-call/payment note is added, which is useful but not behavioral detail about the computation.
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 naming the tool and its purpose, with the payment/trial note appended. No wasted prose, though the free-trial marketing text is only marginally relevant to invocation.
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?
With two undocumented, ambiguously-named parameters, no annotations, and no output schema, the definition omits material an agent needs to call it correctly, particularly which parameter holds the matrix and in what format.
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 coverage is 0% and two parameters ('data' and 'matrix') are entirely undocumented. The description hints that a numeric matrix is the input, but never clarifies the roles of 'data' versus 'matrix', whether they are aliases, which takes precedence, or the expected string encoding.
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 and resource: 'Computes the population covariance matrix over the columns of a numeric matrix.' The word 'population' meaningfully distinguishes it from sibling x402-covariance-sample, though no sibling is named explicitly.
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 when-to-use guidance, no prerequisites, and no explicit alternative named. The 'population' qualifier implicitly contrasts with the sample-covariance sibling, but the agent must infer that routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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