get_context

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

No annotations are provided, so the description carries full behavioral burden. It does disclose the return ordering (most recently updated) and contents, and gives proactive invocation rules. However it doesn't mention whether the search is case-sensitive, fuzzy, or what empty results look like – meaningful gaps for a search tool.

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 purpose first, then returns, then proactive triggers and examples. Dense but every sentence adds actionable value; only the bilingual trigger list feels slightly overloaded.

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 1-parameter read/search tool with no output schema and no annotations, the description covers purpose, returns, and proactive invocation well. Missing only minor behavioral details (pagination, empty results) that an agent could discover at call time.

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 100% and the schema already explains the query searches across key, value, and tags. The description adds an extraction example ("单测" → search "测试") which is helpful but does not extend beyond the schema's own documented scope.

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 (Search) and resource (personal memory by keyword), plus the returned content (preferences, habits, facts sorted by recency). Clearly distinct from siblings like list_contexts or update_context.

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?

Explicitly says when to auto-call (START of coding tasks, user mentions of code quality/style/review) and even provides bilingual trigger keywords with an extraction example. Far exceeds typical when-to-use guidance.

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