Estimates time-varying conditional correlations between industry themes using DCC-GARCH, identifying pairs with strengthening or weakening linkages.
DCC-GARCH 时变条件相关 — 估计行业间动态相关性矩阵，识别联动加强/减弱的行业对。计算较慢(约30s)，返回JSON。
| Name | Required | Description | Default |
|---|---|---|---|
| window | No | 收益率回看窗口(交易日) |
| 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 behavioral disclosure burden. It usefully discloses that computation is slow (~30s) and that it returns JSON, while the estimation language implies a read-only operation. It does not address side effects, data prerequisites, or caching, but the output schema covers return structure.
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 compact and front-loaded with the core purpose, followed by the practical latency caveat. The phrase '返回JSON' is slightly redundant given the output schema, but it does not cause meaningful bloat.
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 computation tool with an output schema, the essential invocation information is present: purpose, parameter default, latency, and return type. However, it lacks guidance on when to choose this tool over related siblings and does not explain how the output relates to the 'strengthening/weakening' identification beyond the name.
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, window, is already fully described in the input schema as the return lookback window in trading days with a default of 120. Since schema coverage is 100%, the description adds no additional parameter semantics beyond what the schema provides, so the baseline of 3 applies.
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?
Description states a specific method (DCC-GARCH), a specific resource (industry dynamic correlation matrix), and the analytical goal (identifying industry pairs with strengthening/weakening linkage). It does not explicitly differentiate from siblings like industry_themes or industry_themes_causality, but the model and objective are distinctive enough for an agent to infer the intended use.
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 explicit when-to-use guidance or mention of alternatives such as industry_themes or industry_themes_causality. The latency warning (~30s) is a practical caveat, not a selection rule, so the agent is left to infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.