set_partner_preferencesSave the partner AI's auto or manual model selection, model ID, and reasoning effort so later native chat collaboration calls can use those settings.
保存另一端 AI 的自动/手动选模、模型 ID 和推理强度，供之后的原生聊天协作调用使用。
| Name | Required | Description | Default |
|---|---|---|---|
| partner_model | No | ||
| primary_agent | No | auto | |
| partner_effort | No | medium | |
| partner_selection | No | auto |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this is a non-read-only, non-destructive, closed-world write. The description adds that the values are persisted for later native chat calls, which is useful persistence context, but it does not say whether saving overwrites prior values, what scope (session vs global) the settings apply to, or what happens if fields are omitted.
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?
A single front-loaded sentence led by the verb, with no filler. It is dense but appropriate for a small setter; only the missing primary_agent detail keeps it from being fully earned.
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 4-parameter, all-optional mutation with no output schema, the description covers most parameters and the persistence purpose, but omits one parameter entirely and gives no indication of write semantics or scope. Annotations carry the safety profile, so the remaining gap is moderate rather than severe.
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?
Schema description coverage is 0%, so the description must carry the load. It names three of the four inputs (selection mode, model ID, reasoning effort), but primary_agent is never explained, and no format or default semantics (e.g. what 'auto' resolves to) are supplied beyond the raw enums.
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?
States a specific verb (保存/save) and resource (partner AI preferences: selection mode, model ID, reasoning effort), and its intended downstream use. It is distinguishable from nearby siblings like show_partner_selector (read) and set_collaboration_mode, though it doesn't explicitly contrast with them.
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 clause '供之后的原生聊天协作调用使用' gives the purpose/context for saving, which implies when the settings matter, but there is no explicit when-to-call guidance, no prerequisites, and no mention of alternatives such as set_collaboration_mode or show_partner_selector.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.