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x402-log-returns

Log Returns: Computes natural-log returns ln(p[k]/p[k-1]) from a price series given as an array or comma-separated string, returning n-1 values 🆓 5 free trial calls per signed wallet

Input Schema

NameRequiredDescriptionDefault
valuesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / properties / values / description
      Removed value: -"Values to process"
  2. Changed1 schema field changed
    • addedInput schema / properties / values
      Added value: +{
      +  "description": "Values to process",
      +  "type": "string"
      +}
  3. First observed

TDQS

B3.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose two useful behaviors: accepted input formats (array or comma-separated string) and output cardinality (n-1 values). It omits any error/edge-case behavior — notably that ln is undefined for non-positive prices — and says nothing about precision or 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One tight sentence, front-loaded with name and formula, then input format, then output size. The promo tail ('🆓 5 free trial calls per signed wallet') is boilerplate that dilutes the payload slightly, keeping it from a 5.

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

Completeness3/5

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

For a single-parameter pure-computation tool with no output schema, knowing the formula, input format, and output length is close to sufficient. However, the log-domain edge cases (zero or negative prices) and the lack of any sibling disambiguation leave gaps an agent would want filled.

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

Parameters3/5

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

Schema coverage is 0% and the single 'values' parameter is typed only as a string, so the description's statement that it accepts an array or comma-separated price series is genuinely additive. It still does not specify separators, minimum length, numeric validation, or whether an actual JSON array is accepted given the string type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb (computes) and resource (natural-log returns) plus the exact formula ln(p[k]/p[k-1]), which is unambiguous. It falls short of a 5 because it never distinguishes itself from the closely-named sibling x402-returns (presumably simple returns), so an agent must guess which return flavor it wants.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not guidance, no mention of the alternative x402-returns or x402-annualized-return, and no note on when log returns are preferred over simple returns. The formula implies usage but does not state it.

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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