kb_learn_url

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

There are no annotations, so the description must carry the full behavioral disclosure for a write operation, and it does state that content is persisted into the knowledge base after body extraction. However, it omits whether re-learning the same URL duplicates or overwrites an entry, whether permissions or auth are required, and what happens when fetching fails or the page is not HTML.

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 summary line is front-loaded and dense, with no filler, and the Args block is compact. The docstring-style parameter list is somewhat mechanical, but each line still adds information rather than echoing the schema.

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?

An output schema exists, so return values need not be explained, and all three parameters are described. For a mutating ingestion tool with no annotations, though, the description should also cover deduplication, size limits, and failure behavior before an agent can invoke it confidently.

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 0%, so the description must document all three parameters, and it does: the URL must be http/https, tags are comma-separated, and title is optional and falls back to the page title when blank — the latter being real meaning the schema does not convey. The only shortfall is the lack of format examples for tags or behavior details about invalid URLs.

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 names a specific three-step action on a concrete resource: fetch a web page, extract the body text, and write it into the knowledge base, and it labels itself as the self-learning entry point. This lets an agent distinguish it from siblings such as web_fetch (which only fetches) or kb_learn_file (which learns local files), though it never names those siblings explicitly.

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 says what the tool does but never says when to use it, when not to use it, or how to choose between it and web_fetch or kb_learn_file. The only hint is the parenthetical '自学习入口', which implies it is the ingestion path but states no conditions.

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