zhiji_workspace_assemble

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

No annotations are provided, so the description carries full burden. It discloses behavioral traits such as latency (10-15 seconds for slow-thinking queries involving LLM inference) and lists returned components. However, it does not explicitly state read-only nature or idempotency, which is acceptable given the context.

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 with the main purpose first, followed by a list of components and a note on latency. It is detailed but not overly verbose; every sentence adds value. Slightly longer than necessary but efficient.

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 absence of an output schema, the description adequately lists the key return components (memoryContext, profile, factContext, etc.) and mentions behavioral aspects (latency for certain queries). For a tool with three parameters and moderate complexity, it is sufficiently complete.

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% with descriptions for all three parameters. The description adds meaning beyond the schema by explaining the query's role, providing default values for userEmail (MB_USER_EMAIL) and maxResults (6), and clarifying usage context.

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 assembles '知己的统一记忆上下文' (unified memory context) for a query, listing specific components (memoryContext, profile, factContext, etc.) and positioning it as a one-stop entry for injecting user understanding into LLM conversations. This distinguishes it from siblings like zhiji_memory_search (memory search only) and zhiji_profile_get (profile only).

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 implies this is the primary comprehensive tool for assembling context ('一站式入口'), but does not explicitly state when to use it versus alternatives, nor does it mention exclusions or prerequisites. The guidance is mostly implicit.

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