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

Since no annotations are provided, the description carries the full burden of behavioral disclosure, and it does well: it explains the risk index meaning, transparency of components, confidence as data completeness, the verdict-free approach ('unknown' over guessing), and the requirement for a mint parameter. However, it doesn't disclose potential rate limits or failure modesspecifically, but these are less critical for a read-only scoring tool. It also includes a disclaimer that it's not financial advice, which is helpful.

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, well-structured paragraph that front-loads the core purpose and value proposition. It is efficient, but includes a mix of technical details and disclaimers. It could be split into bullet points for clarity, but it is concise without redundant repetition.

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 simple tool (one parameter, no output schema), the description covers the key aspects: what it does, how to use it (mint parameter), behavior (unknown over guessing), and differentiation. There is no output schema to explain return values, but it accurately describes the output (0-100 index, components, band, confidence). It is mostly complete for an agent to decide and call 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?

The schema covers 100% of the parameters, including the mint parameter with a full description and an example. The description adds context about the index meaning and that mint is required, but doesn't add parameter-specific semantics beyond what the schema provides. Hence, 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 as computing the SolMachina Machine Risk Index (SMRI) for an SPL token, with a 0-100 scale, transparent components, risk band, and confidence. It explicitly differentiates from siblings by positioning it as the cheap, high-frequency score for agents wanting just the number, and clarifies it is verdict-free and returns 'unknown' over guessing. This distinguishes it from solmachina_token_risk and related tools.

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 states 'for agents that just want the number', clearly indicating the intended use case. It mentions the cheap, high-frequency nature and that it is verdict-free, which implies when to choose this over more comprehensive tools. It does not explicitly name alternatives or exclusion criteria, but the context against siblings is reasonably clear.

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