Append a learning

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

No annotations, so description carries full burden. It discloses that the tool modifies AGENTS.md (auto-loaded memory) and that the action is proactive, plus instructs to inform the user. Lacks details on idempotency or side effects, but adequate for a simple append.

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?

Two sentences, no fluff, front-loaded with action. Every sentence adds value.

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?

No output schema, so description should mention return behavior. It doesn't state what the tool returns or if it creates the file if missing. Could be more complete regarding preconditions and outcome.

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 0%, so description must explain parameters. It implies 'text' is the learning content and 'project' is the identifier, but does not provide explicit format or constraints. Acceptable given simplicity of 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?

Clearly states the tool appends a dated bullet under '## Learnings' in AGENTS.md. Distinguishes from sibling log_issue by specifying that this is for general learnings, not specific bugs.

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 instructs when to call proactively upon discovering durable gotchas/workarounds, and provides an alternative for specific bugs (log_issue). No ambiguity.

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