Computes Pearson and Spearman correlations between gas costs and volatility from two caller-supplied, time-aligned series to reveal how gas price changes relate to market volatility.
Pearson and Spearman correlation between gas costs and volatility.
| Name | Required | Description | Default |
|---|---|---|---|
| gas | Yes | ||
| vol | Yes | ||
| asset_class | No | Which market answers. Inferred from the symbol's source when omitted. |
Changes observed during successful MCP inspections.
v0.3.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It does not state whether this is a read-only computation, what the return shape is, how misalignment or missing data is handled, or whether the result includes p-values or only correlation coefficients. For an analysis tool with no output schema, these omissions matter.
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 a single front-loaded sentence with no filler. It is concise, though its brevity borders on under-specification rather than optimal economy. Structurally there is no clutter or burying of the main point.
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?
The tool computes correlations from two aligned data series, but the description does not explain the return value, minimum data requirements, or alignment expectations beyond what the schema object description separately states. With no annotations and no output schema, the description is too incomplete for an agent to call the tool confidently without studying the schema closely.
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 tool description says nothing about the three parameters. Schema description coverage is 33%, with asset_class documented but gas and vol lacking property-level descriptions. The schema's object-level description does explain alignment on local_ts, which partially helps, but the tool description adds no parameter semantics and does not compensate for the low coverage.
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 a specific computation: Pearson and Spearman correlation between gas costs and volatility. This is clear enough to distinguish the tool from most siblings such as basis, funding-apr, or vol-skew. It stops short of explicitly naming when this is preferred over a generic correlation tool, but the purpose itself is unambiguous.
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 gives no guidance on when to use this tool versus alternatives. It does not mention prerequisites such as aligned series, minimum sample size, or the asset_class parameter. An agent must infer the usage context entirely from the name and schema.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.