log_error

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

No annotations are provided, so the description carries full burden. It mentions recording to a local database but does not detail side effects (e.g., whether duplicates are overwritten, or security considerations). Adequate for a straightforward logging tool but could be more transparent.

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 a single, concise sentence with a relevant emoji. It is front-loaded with the action and resource, and contains no unnecessary words.

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 parameter count (5), no output schema, and no annotations, the description provides sufficient context for the tool's purpose and when to use it. However, it lacks details on the database behavior (e.g., how entries are stored or retrieved later), which could be helpful for full understanding.

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%, so baseline is 3. The description adds no additional meaning beyond the schema descriptions; it only restates the purpose. The parameter details are well covered by the schema itself.

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 (ghi nhận/record), resource (lỗi biên dịch MQL5 vào cơ sở dữ liệu cục bộ), and usage purpose (thu thập lỗi thường gặp và giải pháp). It effectively distinguishes from sibling tools like list_common_errors (listing) and diagnose_error (diagnosing).

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 explains the context of use ('Dùng để thu thập các lỗi thường gặp và giải pháp'), implying when to log errors. While it does not explicitly state when not to use or name alternatives, the sibling list provides enough differentiation for an AI agent.

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