analyze

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 effectively describes key behaviors: the tool analyzes local image files, supports both image understanding and OCR capabilities, allows flexible AI configuration, and provides examples of how it responds. It mentions server configuration dependencies but doesn't cover error handling, rate limits, or authentication requirements.

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 well-structured with clear sections: purpose statement, usage context, capabilities list, and example. While comprehensive, it could be slightly more concise - the capabilities section repeats information from the purpose statement, and the example is detailed but necessary. Most sentences earn their place by adding value.

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 3 parameters, no annotations, and no output schema, the description provides substantial context about what the tool does and how to use it. It covers purpose, usage guidelines, capabilities, and provides a concrete example. The main gap is the lack of information about return values or error conditions, which would be helpful given the absence of an output schema.

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 100%, so the schema already fully documents all three parameters. The description adds minimal additional parameter semantics beyond what's in the schema - it mentions the types of questions that can be asked and provides an example, but doesn't add significant meaning beyond the comprehensive schema descriptions.

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: analyzing pre-existing image files using AI models. It specifies the exact action ('analyzes'), resource ('image file from local filesystem'), and scope ('using configured AI model'). It distinguishes from sibling tools 'image' and 'list' by focusing on analysis rather than image manipulation or listing operations.

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 explicit guidance on when to use this tool: 'when an image already exists (e.g., previously captured, downloaded, or generated) and you need to understand its content, extract text, or answer specific questions about it.' It also distinguishes capabilities (image understanding, OCR) and provides clear examples of appropriate use cases, making it easy to determine when this tool is the right choice.

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