limit_up_calibration_latestGet the most recent empirical calibration results, including recommended weights, factor AUC, benchmark rate, and sample size, for dashboard display.
返回 reports.db 中最新一份实证校准结果（推荐权重/因子AUC/基准率/样本量），前端看板展示用。
| Name | Required | Description | Default |
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Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description has the full burden of behavioral disclosure. It does disclose the data source (reports.db), the fact that it returns the latest one record, and the nature of the payload (calibration metrics), which implies a read-only operation. It does not explicitly state that no data is modified, what happens if reports.db has no calibration rows, or whether the record is ordered by time.
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 sentence that packs the source database, the selection criterion ('latest'), the returned fields, and the intended display use without any redundant words. Essential information is front-loaded and every clause contributes.
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 parameterless read tool with no output schema, the description is largely complete: it names the data source, the record selection ('latest'), the output fields, and the use case. It could additionally mention empty-result behavior or a timestamp, but the core invocation knowledge is present.
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 input schema has zero properties, so there are no parameters to describe. The 100% schema description coverage is vacuous rather than meaningful, and the description adds no parameter semantics, but no such semantics are needed for a parameterless tool. Baseline 4 is appropriate.
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 uses a specific verb ('返回' / returns) and a concrete resource: the latest empirical calibration record from reports.db, listing key fields such as recommended weights, factor AUC, baseline rate, and sample size. It makes the tool's function clear, but it does not explicitly distinguish it from sibling tools like limit_up_latest or limit_up_calibrate, so it falls just short of full sibling differentiation.
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 phrase '前端看板展示用' (for front-end dashboard display) indicates a clear consumption context, implying this is for reading a prepared result rather than generating one. However, it never states when to prefer this tool over limit_up_latest or limit_up_calibrate, nor does it mention when not to use it, leaving the agent to infer the boundary between calibration reading and calibration generation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.