local_mcp_append_optimization_log

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

No annotations are provided, so the description must carry full behavioral transparency. It lists what the log records but does not disclose side effects, idempotency, append-only nature, or whether it modifies existing logs. Key behavioral traits are missing.

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 sentence that efficiently states the purpose and scope. It is front-loaded with the verb and key contents. Could be more structured with bullet points for clarity, but remains concise.

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 13 parameters, no output schema, and no annotations, the description is too brief to provide complete context. It does not specify scope (per-session/project/user), required permissions, or return behavior. Important for an AI agent to use correctly.

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 31% (only roundType and outcome have examples). The tool description lists high-level categories but does not explain most parameters' meaning, structure, or constraints. For 13 parameters with low schema coverage, the description fails to compensate.

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 appends an optimization log and lists the types of information recorded (user ideas, Codex decisions, skills referenced, file changes, verification results, safety boundaries). It distinguishes from siblings like local_mcp_read_optimization_log and local_mcp_optimization_log_status.

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 use after an optimization round but does not explicitly state when to use it vs alternatives, nor provide prerequisites or exclusions. The sibling tools suggest differentiation but description lacks explicit guidance.

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