runninghub_search_models

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

No annotations are provided, so the description carries the full behavioral burden. It implies a safe read operation and discloses the return content (endpoints plus descriptions) and a filterable catalog size, but says nothing about pagination behavior, ordering, or result caps beyond the limit parameter.

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?

Front-loads the core purpose and catalog scale, then the category legend, then the next-step tool. Every sentence is functional, though the category enumeration could arguably live in the enum description.

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?

With no output schema, the description must convey the return shape, and '返回可用的 endpoint 与说明' is minimal — enough to know endpoints come back, but not the result structure or ordering. It covers the essential next step but leaves gaps an agent would need to probe.

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 100%, so the baseline is 3. The description does add value by glossing the category enum (image=图像、video=视频、audio=音频、other=其他), but it adds nothing for keyword, group, or limit beyond what the schema already documents.

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 (搜索) and resource (模型目录), plus scope details (350+ 标准模型 API 端点) and what is returned (endpoint 与说明). However it never distinguishes itself from the sibling runninghub_list_models, which an agent could easily confuse with a search tool.

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 tool-to-tool handoff is explicit — 找到模型后用 runninghub_submit_task 提交任务 — which is genuinely useful routing. But there is no guidance on when to use this versus runninghub_list_models, nor any exclusion conditions, so the core selection decision is left to inference.

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