get_analysis_packet

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does add some useful context ('backend 先完成大部分工作', and the N225 execution-target restriction), but it does not disclose side effects such as whether save_analysis=true persists data, what the return value contains, or any latency/caching implications. For a tool with no annotations Magnitude this is a notable gap.

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, scannable, and free of filler. The parameter lines are front-loaded and informative. The opening phrase is slightly tautological, but the added parenthetical about backend precomputation earns its place. Overall, every line contributes useful information.

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?

Given no annotations according to outline and no output schema, the description should explain what an analysis packet is, what it returns, and what side effects may occur. It does not mention the save_analysis behavior curves or the format of the response, and it offers no differentiation from sibling analysis tools. The description is parameter-focused but contextually incomplete for a tool with this little structured metadata.

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 0% description coverage curves, but the description compensates by enumerating valid values for market (osaka | taiwan), horizon (1d/2d/5d/10d), detail_level (compact | normal | audit), and giving a concrete target example. The only missing parameter is save_analysis, whose meaning is not addressed at all. This is strong added value over the bare schema.

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 identifies the tool as '正式分析封包' (official analysis packet) with a note that the backend does most work first, which implies a precomputed analysis retrieval. This is enough to know the general resource, but it does not clearly distinguish itself from siblings like get_official_release_snapshot, get_analysis_archive_status, or analyze_osaka_nikkei/analyze_taiwan_stock. It reads more like a label than a complete functional definition.

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 provides parameter usage constraints, such as valid market/horizon/detail_level values and the warning that ^N225 can only be PROXY/REFERENCE, but it gives no guidance on when to use this tool versus alternatives. There is no explicit mention of situations where another sibling should be chosen instead.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.