quantum_scorecard

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

With no annotations, the description fully covers behavioral traits: read-only, heavy RPC usage, sampling methodology, and output details (exposure breakdown, balance-at-risk).

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?

Packed with useful information, front-loaded with purpose and scope. Could reduce wordiness slightly, but every sentence contributes meaning.

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?

Despite 6 parameters and an output schema, the description explains high-level outputs (exposure classes, grade tiers) and usage context (sample vs census, heavy RPC). Output schema exists, so return format is covered.

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 only 17% (only accounts described). The description mentions cap, network, accounts, markdown, maxPages, sampleLedgers but adds minimal semantics beyond their names. Some context (e.g., markdown for public report) is helpful, but incomplete.

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 explicitly states it's a 'Network-level Quantum Readiness Scorecard' aggregating HNDL exposure and config grade, clearly distinguishing it from sibling tools like hndl_exposure and quantum_grade which focus on individual components.

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?

Provides clear context: operates on a sample, not a ledger census, and notes sampling bias. Does not explicitly state when not to use or name alternative tools, but the limitations are well-communicated.

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