quantaoptima_log_action

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

Annotations are absent, so the description carries the full behavioral burden and does so well: it discloses the signing algorithm, the hash-chain linkage, tamper-evidence semantics (tampering invalidates the chain from that point forward), and tier availability. It omits failure modes, auth requirements, and whether logging is synchronous, but the core behavioral profile is unusually explicit.

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?

Front-loaded with the purpose and the security guarantee before the Args/Returns sections, and the two-sentence chain explanation earns its place. The six-item bullet list of loggable things is somewhat padded but functions as concrete usage examples.

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?

With an output schema present, return values need no further explanation, and the Args section covers every input. The remaining gap is workflow context – no prerequisites, auth model, or relationship to the sibling verify/export/status tools for a 5-param mutating tool.

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, and it does: each of the five parameters gets an explanation (action_type with concrete example values, state_before/state_after as JSON of input/output, metadata as extra tags/params, actor defaulting to "ai-agent"). This adds real meaning beyond the bare titles and types in the schema.

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?

States a specific verb (log) and resource (action to the cryptographic audit chain), and the mechanism (HMAC-SHA256 signed, hash-chained) immediately tells the agent this is the write/append tool in a family that otherwise verifies, exports, and reports status. An agent can distinguish it from quantaoptima_verify_chain and quantaoptima_export_chain without opening any schema.

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?

"Use this to make any AI agent workflow auditable" plus an explicit enumeration of what to log (decisions, API calls, file operations, transformations, queries, tool invocations) gives clear context for when to reach for it. It stops short of naming alternatives or exclusions – nothing says when NOT to log, or how this relates to quantaoptima_audit/verify_chain in a workflow.

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