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

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Get' implies a read operation, the description doesn't specify whether this requires authentication, has rate limits, returns real-time or cached data, or what format the information comes in. For a tool with zero annotation coverage, this represents significant gaps in behavioral transparency.

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 perfectly concise - a single sentence that efficiently communicates the tool's purpose without any wasted words. It's front-loaded with the core functionality and includes specific details about what information is retrieved.

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 operation with no parameters, the description covers the basic purpose adequately. However, with no annotations and no output schema, the description doesn't address important behavioral aspects like authentication requirements, response format, or data freshness. The description is complete enough for the tool's simplicity but leaves gaps in operational context.

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 tool has zero parameters, and schema description coverage is 100% (though trivial since there are no parameters). The description appropriately doesn't discuss parameters since none exist. A baseline of 4 is appropriate for a zero-parameter tool where the schema fully documents the parameter situation.

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's purpose with a specific verb ('Get') and resource ('account information'), including what information is retrieved ('plan details and credit usage'). It doesn't differentiate from its only sibling 'remove_background', but that's reasonable since they serve completely different functions.

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 provides no guidance on when to use this tool versus alternatives. While there's only one sibling tool ('remove_background') that serves a completely different purpose, the description doesn't mention any prerequisites, constraints, or contextual factors for using this tool.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that it 'Returns the processed image as base64,' which adds some context about the output format. However, it lacks details on performance (e.g., processing time, rate limits), error handling, or side effects (e.g., whether the original image is modified or stored). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 and front-loaded, consisting of only two sentences that directly state the tool's function and key input/output details. Every sentence earns its place by covering essential information without any redundancy or unnecessary elaboration, making it efficient and 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?

Given the complexity of a 6-parameter image processing tool with no annotations and no output schema, the description is somewhat incomplete. It covers the basic purpose and input/output formats but lacks details on behavioral aspects, usage context, and output specifics beyond base64 encoding. While it's minimal, it provides enough to understand the core function, but more context would be beneficial for full comprehension.

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 description coverage is 100%, meaning all parameters are well-documented in the input schema. The description adds minimal value beyond the schema by mentioning that it 'Accepts URL or base64-encoded image,' which relates to the 'image' parameter but doesn't provide additional semantics. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

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's purpose: 'Remove the background from an image.' It specifies the verb ('remove') and resource ('background from an image'), making the function unambiguous. However, it doesn't differentiate from the sibling tool 'get_account', which is unrelated, so it doesn't need sibling differentiation but could be more specific about the processing method.

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 provides no guidance on when to use this tool versus alternatives. It mentions that it 'Accepts URL or base64-encoded image,' but this is about input formats, not usage context. There's no indication of prerequisites, limitations, or scenarios where this tool is preferred over other image processing methods.

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