analyze_image

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. It adds useful context about supported/unsupported image formats and important handling of the '@' prefix in file paths, which goes beyond basic functionality. However, it doesn't disclose other behavioral traits like rate limits, authentication needs, error handling, or what 'powerful LLM' entails operationally, leaving gaps for a tool with mutation-like analysis capabilities.

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 appropriately sized and front-loaded with core functionality in the first sentence. However, it includes some redundancy (e.g., repeating 'LLM' and format lists) and the 'Args:'/'Returns:' section headers add structure but aren't strictly necessary. Most sentences earn their place, particularly the detailed parameter explanations, though minor trimming could improve efficiency.

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 tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is reasonably complete. It covers purpose, parameters, format constraints, and return values ('Text content with the image analysis result'). The main gap is lack of behavioral details like error cases or LLM-specific limitations, but it provides enough context for basic usage without being overwhelming.

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 description adds substantial meaning beyond the input schema, which has 0% schema description coverage. It clearly explains both parameters: 'prompt' as 'the text prompt describing what you want to analyze or extract' and 'image_source' with detailed examples of URL formats, local paths, and critical handling of the '@' prefix. This fully compensates for the schema's lack of documentation, providing complete parameter semantics.

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: 'analyze and understand image content from files or URLs' and specifies it uses an LLM. It distinguishes the action (analyze) and resource (image content), though it doesn't differentiate from siblings since none exist. The initial sentence is slightly redundant with 'powerful LLM' but still communicates core functionality effectively.

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 some usage context: 'Use this tool to analyze images by LLM' and specifies supported formats (JPEG, PNG, WebP) while listing unsupported ones. However, it lacks explicit guidance on when to use this tool versus alternatives (no siblings exist) or any prerequisites beyond format constraints. The guidance is implied rather than explicit.

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