index_module

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

With no annotations, the description carries the full behavioral burden. It clearly says the operation is an append (追加进向量库), notes an auth/prerequisite need (OpenAI key), and declares a return value ({count}). It does not discuss deduplication or overwrite behavior, but the core side-effect is transparent enough.

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 tight and front-loaded: it opens with the action, then gives metadata, usage context, prerequisite, and return value in two compact sentences. No words are wasted.

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 tool with six parameters, no annotations, and no output schema, the description provides the general workflow but leaves gaps: ruleset is unnamed, the semantic of the returned count is ambiguous, and there is no explanation of what constitutes a chunk. It is adequate but not enough to fully handle all invocation nuances.

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 no descriptions (0% coverage), and the description partially compensates by listing the metadata group 'campaign/source/license/note_id' and implying text is the prose to index. However, the ruleset parameter is entirely omitted and the precise value semantics of each metadata field are not explained, so it only modestly bridges the coverage gap.

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 clearly states the action: '把一段模组散文切块嵌入并追加进向量库' (chunk module prose and append into the vector DB) and gives the purpose of making content retrievable via semantic_search by campaign. This is specific enough to distinguish from sibling tools like semantic_search and create_note.

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 gives clear context for when to use the tool—when you want indexed module prose to be semantically searched—and names a prerequisite (OpenAI key). However, it does not explicitly compare against alternatives or say when not to use it, so it misses the 'when-not' layer.

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