VNISH Global Operator

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

The description adds value beyond the readOnlyHint annotation by clarifying that the tool is non-authoritative (scenario, not forecast) and that it does not supply actual fees or market data. It also highlights common pitfalls like double-counting the fee, which is helpful behavioral context not present in the schema or 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 concise and front-loads the purpose, then provides the formula and critical usage notes. The formula is compact, and the warnings are essential. Slight improvement could be separating the formula for readability, but it's not overlong.

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?

For a tool with six parameters and no output schema, the description covers the purpose, formula, and key caveats. It doesn't specify the return format, but given the complexity and that it's a calculation tool, the description is sufficient for an agent to invoke it correctly. The warning about double-counting is particularly important.

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 already provides 100% coverage of all six parameters with descriptive text, so the description doesn't need to repeat them. However, the description adds a formula that clarifies how parameters interact, which is useful, but baseline is appropriate given high schema coverage.

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 calculates a hypothetical daily revenue scenario based on user-supplied inputs, specifying the formula and the scope (illustrative, not a forecast). It distinguishes from siblings by being a standalone calculation tool, not a lookup or reference tool.

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 when to use the tool (for hypothetical scenario calculations) and provides key guidance: use pre-fee gross hashrate, don't deduct the fee twice. It doesn't explicitly name alternatives or when NOT to use it, but the context makes it clear it's not for actual profit forecasting.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds value by explicitly stating 'No device connection or action is performed,' which reinforces the read-only nature and clarifies that no side effects occur. It also notes the guide is public, implying no auth needs, which is useful context beyond 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?

Three sentences with no waste. The primary purpose is front-loaded, followed by a clarifying exclusion and a final note on behavior. Every sentence earns its place, and the structure is easy to parse.

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?

For a simple lookup tool with two parameters and strong annotations, the description covers the purpose, the scope of topics, the non-device-specific nature, and the no-action behavior. It does not mention return format or the language parameter, but the schema covers those. The description is largely complete for an agent to invoke correctly, though a bit more on the language parameter would make it fully complete.

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. It mentions the three topics (control-board identification, installation SSH errors, return-to-stock preparation) which align with the topic enum, giving meaning to that parameter. However, it does not explain the language parameter or its default, and it does not explicitly map the topics to the enum values. The description adds some context but not full parameter semantics for both parameters.

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 states a specific verb ('Return') and resource ('a selected public VNISH Ninja guide'), and enumerates the exact topics covered. It distinguishes itself from siblings by clarifying it is a reference, not a device-specific instruction, so an agent can tell it apart from calculate_roi_scenario and lookup_firmware without inspecting schemas.

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?

It clearly states that the tool is not for device-specific installation or recovery instructions, providing a clear exclusion. However, it does not explicitly name alternative tools or conditions for when to use this tool vs. others, though the exclusion implies the boundary. The context is clear enough for typical use.

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

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

Beyond the annotations, the description adds meaningful behavioral context: results come from a dated snapshot, records do not establish compatibility, authenticity, or successful testing, and the tool does not install anything. These caveats are exactly the kind of context an agent needs before relying on lookup results.

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 compact and well-ordered: purpose first, then usage guidance, then return contents, then caveats. Every sentence adds distinct value, and there is no redundant restatement of the title or schema.

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?

For a simple read-only lookup with one required parameter and no output schema, the description is complete: it states what the tool returns, how to narrow the query, what the data represents, and what the tool does not do. Nothing needed for correct invocation is missing.

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?

With schema description coverage at only 33%, the description compensates by revealing the role of each parameter family: model_id is the exact lookup key, while board_platform and version are optional narrowers. It does not explain the enum meanings, but the schema already enumerates those values.

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 uses a specific verb ('Find') and resource ('records in a dated snapshot of the public vnish.global firmware catalog'), which clearly identifies what the tool does. It also states the tool does not download or install firmware, removing ambiguity and differentiating it from broader firmware-management actions.

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 gives clear invocation guidance: use the exact model ID and narrow by board code and version if known. It also states a key limitation ('Does not download or install firmware'), which helps an agent avoid using it for installation, but it does not explicitly name sibling alternatives or exclusion conditions.

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