vm_fidelity_report

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

The description discloses key behavioral details: it loads a corpus, runs bytecode, compares direction, computes agreement only over comparable runs, excludes degraded runs, states it is strictly offline, and handles empty corpus. This is comprehensive given no annotations.

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 fairly concise given the complexity, with a clear structure: purpose, method, calculation details, and caveats. It could be slightly tighter but remains effective.

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?

The description covers all necessary aspects: what it does, how it works, edge cases (empty corpus), and the metric calculation. An output schema exists, so no need to detail return values. It is complete for decision-making.

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 single parameter 'includeMismatches' is already described in the schema. The description adds no additional meaning beyond what the schema provides. With 100% schema coverage, baseline 3 is appropriate.

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 identifies the tool's purpose: measuring fidelity of the local Hook VM against on-chain results. It uses specific verbs like 'measures how faithfully... reproduces' and distinguishes it from sibling tools by focusing on a specific metric.

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 what the tool does and how it works, providing enough context for when to use it. However, it does not explicitly state when not to use it or reference alternatives among the many sibling tools, which would improve clarity.

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