get_model_catalogIdentifies the primary chat AI and fetches candidate models, reasoning levels, and saved settings for the collaborating AI, so both sides can coordinate model selection.
识别主对话 AI，并读取另一端协作 AI 的候选模型、推理强度和已保存设置。
| Name | Required | Description | Default |
|---|---|---|---|
| workspace | No | 当前原生聊天所打开工作区的绝对路径；每次调用按当前聊天动态选择，不绑定安装目录。 | |
| primary_agent | 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 readOnlyHint=true and openWorldHint=false. The description adds data scope by naming candidate models, reasoning intensity, and saved settings, but does not add further behavioral context such as auth needs, rate limits, or side effects.
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 with no filler. Its brevity is efficient, though it leaves important usage and parameter context unaddressed.
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?
Read-only annotations and the workspace schema cover safety and path context, and the description names the returned data. However, the lack of usage routing and primary_agent parameter detail leaves gaps for selecting among sibling tools.
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 coverage is 50%; workspace is described in the schema, but primary_agent has no schema description. The description hints at '主对话 AI' but does not explain the auto/codex/claude enum values or how primary_agent affects the catalog retrieval.
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 specific verbs (识别/读取) and resources (主对话 AI、候选模型、推理强度、已保存设置). It does not explicitly differentiate from siblings like show_partner_selector or set_partner_preferences, but the core purpose is clear.
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 when-to-use, when-not, or alternative tool is mentioned. The agent must infer the appropriate context from the name and sibling tools, with no routing guidance in the definition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.