get_correlationHow closely an asset's social activity tracks its price, with the lead/lag.
Correlation is not causation and this endpoint does not claim it is. A
high coefficient says the two series moved together over the window —
it does not say which one moved first. Use get_leading_indicators for
that question.| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already cover read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context: the coefficient is window-based, measures co-movement, and makes no causal or lead/lag claim. It does not describe the output shape or window details, and 'with the lead/lag' creates some potential confusion, so it is not a perfect 5.
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 short and front-loaded: the core purpose appears first, followed by the caveat and sibling routing. Every sentence contributes value and the length is appropriate. The slight ambiguity around 'with the lead/lag' keeps it from being a perfect 5.
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?
For a one-parameter read-only endpoint, the description gives the core purpose, causality caveat, and the correct alternative tool, while annotations cover the safety profile. However, with no output schema and no parameter guidance, it leaves the output object shape, window definition, and symbol format unspecified, so the description is not fully self-contained.
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 only parameter, symbol, has 0% schema description coverage. The description never explains what symbol formats are accepted or provides an example, so it does not compensate for the schema's silence. The phrase 'an asset's social activity' weakly implies symbol identifies an asset, but that is not enough to give the agent concrete parameter guidance.
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 first sentence clearly identifies the tool's resource and question: how closely an asset's social activity tracks its price. It also distinguishes itself from get_leading_indicators by stating that the correlation does not say which series moved first. However, it lacks a direct command verb like 'computes' or 'returns', and the phrase 'with the lead/lag' is slightly ambiguous given the later disclaimer.
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 explicitly says the endpoint does not claim causation and does not say which moved first, then directs the agent to use get_leading_indicators for that question. This is a clear when-not-to-use statement with an explicit named alternative. The positive use case, measuring co-movement over a window, is also implied clearly by the first sentence.
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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