learn_text

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool adds text to a knowledge base for future reference, it lacks critical details such as whether this operation is idempotent, what permissions are required, how the text is processed (e.g., embedding generation), or potential side effects like storage limits. This is a significant gap for a mutation tool with zero annotation coverage.

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 well-structured and front-loaded with the core purpose, followed by usage scenarios and parameter explanations. Every sentence adds value without redundancy, and it efficiently conveys necessary information in a compact format, making it easy for an agent to parse.

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?

Given the tool's complexity (mutation with 2 parameters), no annotations, and an output schema (which reduces the need to describe return values), the description is moderately complete. It covers purpose, usage, and parameters but lacks behavioral details like error handling or processing behavior. This is adequate but has clear gaps for a tool that modifies a knowledge base.

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 description adds meaningful context for both parameters beyond the input schema, which has 0% description coverage. It explains that 'text' is the content to learn and store, and 'source_name' is a descriptive name for the source with examples like 'user_notes' or 'research_paper'. This compensates well for the schema's lack of descriptions, though it could provide more detail on format constraints.

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 tool's purpose with specific verb ('添加'/'add') and resource ('RAG 知识库'/'RAG knowledge base'), and distinguishes it from siblings like ask_rag (querying) and clear_embedding_cache_tool (maintenance). It explicitly defines the action as adding new text for future reference, making the purpose unambiguous.

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 provides clear usage scenarios (e.g., adding facts, storing conversation info, saving research notes), which helps the agent understand when to use this tool. However, it does not explicitly state when NOT to use it or mention alternatives like ask_rag for retrieval, leaving room for improvement in distinguishing from sibling tools.

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