record_attempt

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It notes that the tool 'updates reliability aggregates', indicating a mutation side effect. However, it does not elaborate on the extent of the update, idempotency, required permissions, or potential errors. More detail would be beneficial for an agent to assess risks.

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 extremely concise: two sentences plus an inline example. The first sentence states the core purpose, the second expands on usage, and the example reinforces understanding. No redundant or vague wording. Ideal structure for quick comprehension.

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 5 parameters and an output schema (not shown), the description covers the purpose and usage well. It does not describe the return value, but the presence of an output schema reduces that burden. It might benefit from mentioning error cases or failure modes, but overall it is sufficient for a self-test recording 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?

The input schema has 0% description coverage, so the description must compensate. It explains the purpose of parameters: category as test name, success as boolean, optional note and probe_id, and latency_ms. The example demonstrates parameter order and usage concretely. While latency_ms is not explicitly explained, the example implicitly covers it.

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 records self-test outcomes and updates reliability aggregates. It distinguishes between registered probes and ad-hoc observations, providing a concrete example that illustrates its use. This specificity sets it apart from sibling tools like register_probe or run_probe.

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 explicitly instructs when to use the tool: for both registered probes (pass probe_id) and ad-hoc observations. It gives a real-world example of an ad-hoc use case. While it doesn't explicitly state when not to use it, the context is clear enough for an agent to decide. Alternatives are implied by sibling names but not directly compared.

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