generate_cloud_log

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

With no annotations, the description carries the full burden. It discloses that the tool produces CLOUD.md and includes certain content sections, but it does not explicitly state that it overwrites an existing file, requires an existing project, or performs any reads of project files. This is adequate but not rich.

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 concise and front-loaded with the main purpose. However, it only documents one parameter, which is a completeness issue rather than a structure problem. The sentence is efficient, but the parameter section is incomplete.

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 9 parameters and no annotations, the description is incomplete. It explains what the log contains but does not explain what each optional parameter does, how they affect generation, or any prerequisites. An agent would be uncertain about how to pass meaningful values for fields like 'important_decisions' or 'known_limitations'.

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?

Only project_path is described in the Args section. The other 8 parameters are left without explicit descriptions, despite the narrative mentioning content categories that match them. Schema coverage is 0%, so the description does not compensate for the undocumented 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 verb ('(Re)generate') and the resource ('CLOUD.md -- the AI work log') with a specific explanation of what it documents (goal, architecture decisions, files created/updated, implementation notes, known limitations, future work). This distinguishes it from sibling tools like generate_readme or generate_project_tree.

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 usage: you call this when you need to create or refresh the AI work log for a project. It provides clear context for what the tool is for, though it does not explicitly address alternatives or exclusions.

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