get_client_info_json

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

No annotations are provided, so the description carries the full burden. It discloses the return format (structuredContent) and that the default dataset is created on demand, implying a potential side effect. However, it does not explicitly state whether the tool is idempotent or whether creating the dataset requires any authorization. The behavior is simple enough but could be more explicit.

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?

Three sentences, each with a clear purpose: core function, usage context, return shape. No redundant information. Front-loaded with the essential action.

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 zero parameters and no output schema, the description covers the return structure, use case, and on-demand creation. It could mention what happens if the client is unknown, but it is fairly complete for a simple tool.

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 tool has zero parameters, and the input schema is fully described (100% coverage). The description adds value by explaining the return shape, which is beyond the schema's scope. Per guidelines, baseline for 0 params is 4, and the description meets that.

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 returns the MCP client identity and agent-scoped default dataset. The verb 'Return' and specific resources are unambiguous. It distinguishes from sibling tools like list_datasets_json by focusing on client identity rather than dataset listing.

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 context on when this is used (workspace UI for memory separation per agent) and gives examples (Cursor, Claude Code). However, it lacks explicit 'when to use' vs 'when not to use' guidance, though the purpose is clear enough to avoid confusion with siblings.

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