bluemouse

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a write operation ('寫入' - write) which implies mutation, but doesn't disclose critical behavioral traits: what permissions are needed, whether files are overwritten or merged, if there are rate limits, what happens on failure, or whether this is a destructive operation. The description adds minimal context beyond the basic action, leaving significant gaps for a tool that writes files to a workspace.

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 appropriately sized and front-loaded: the first sentence states the core purpose clearly. The second sentence adds conceptual context but could be considered slightly verbose. The parameter and return sections are structured but not excessively detailed. There's minimal waste, though the metaphorical language ('寄生'到『交付』' - parasitic to delivery) might not be immediately clear to an AI agent.

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 has 3 parameters with 0% schema coverage, no annotations, but has an output schema (so return values are documented elsewhere), the description is moderately complete. It covers the basic purpose and parameter meanings, which is adequate for a tool with output schema support. However, for a file-writing operation with mutation implications, it should ideally include more behavioral context (permissions, overwrite behavior, error handling) given the lack of annotations.

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 description coverage is 0%, so the description must compensate. It provides parameter names and basic semantics: 'project_name: 項目名稱' (project name), 'files: 文件映射 (JSON字符串)' (file mapping as JSON string), and 'metadata: 元數據 (JSON字符串)' (metadata as JSON string). This adds meaningful context beyond the bare schema, explaining what each parameter represents. However, it doesn't specify format details (e.g., JSON structure for files/metadata) or constraints, leaving some ambiguity for the 3 parameters.

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: '將生成的項目文件寫入宿主工作區' (write generated project files to the host workspace). It specifies the verb ('寫入' - write) and resource ('項目文件' - project files), though it doesn't explicitly differentiate from sibling tools like 'mmla_create_node' or 'mmla_update_status' which might also involve file operations. The metaphorical language about completing a 'parasitic to delivery' cycle adds context but doesn't detract from the core purpose clarity.

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 no guidance on when to use this tool versus alternatives. It mentions completing a 'parasitic to delivery' cycle, which implies this is a final step in a workflow, but doesn't specify prerequisites (e.g., should 'check_bluemouse_environment' be run first?) or when to choose sibling tools like 'mmla_create_node' for creation versus this tool for delivery. There's no explicit 'when' or 'when not' guidance for the agent.

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