analyze_local_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 does state that analysis runs locally and is privacy-focused, which is a key trait. However, it omits other critical behaviors such as requiring a loaded model, the nature of the response, or any side effects, leaving significant gaps.

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 sentence that efficiently conveys the tool's purpose and primary differentiator (privacy). It is front-loaded and free of fluff, though it could include a bit more operational detail without becoming verbose.

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 no output schema and no annotations, the description is insufficiently complete. It does not explain what the tool returns, how the prompt interacts with the image, or what prerequisites exist (e.g., models loaded). Given the rich sibling context, more detail is necessary for an agent to invoke the tool 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?

Schema description coverage is 67%, with prompt and image_path described. The description adds value by naming example models (Llava, Moondream) that give meaning to the undocumented 'model' parameter, but it does not elaborate further on parameter semantics. This partial compensation results in a mid-range score.

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 privacy-focused image analysis using local vision models, naming specific models like Llava and Moondream. It distinguishes it from sibling tools by emphasizing local execution and privacy, though it does not explicitly mention the image_path and prompt inputs or the textual output.

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 implies a use case for privacy-sensitive image analysis but provides no explicit when-to-use guidance or alternatives. It does not mention the async sibling tool analyze_local_image_async or other local LLM query tools, leaving the agent without direction on choosing this over alternatives.

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