get_qa_context

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

With no annotations provided, the description fully covers behavioral traits: fallback to built-in knowledge, case-insensitive partial section matching, and the output structure (whole file with section list when no section specified). No contradictions.

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 reasonably concise in Chinese (4-5 sentences), front-loading the core purpose and usage. While every sentence adds value, it could be slightly more compact without losing meaning.

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 one optional parameter, no output schema, and no annotations, the description is fully adequate. It explains fallback, usage, and output, making the tool's behavior predictable for an AI agent.

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 single optional parameter 'section' has 100% schema description coverage. The description adds significant value by explaining matching behavior (case-insensitive, partial match) and the effect of omission vs. specification, which goes beyond the schema.

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 reads a project-specific qa-knowledge.md file, which distinguishes it from sibling tools like init_qa_knowledge (which creates the file). The verb '讀取' (read) and specific resource are well-defined.

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 explicitly outlines the usage pattern: call this tool first to get knowledge, then pass sections to generate_test. It also notes fallback behavior when the file doesn’t exist. While it doesn’t explicitly state when not to use it or list alternatives, the guidance is clear for the intended workflow.

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