Analyze supplied AI answers

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

Annotations already mark this as read-only and closed-world, and the description adds useful behavioral context: no model calls, no automatic recommendation labeling, and separation of prompt types and failures. It does not contradict the annotations, though it omits any return-format details.

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 two tightly written sentences with the core scope front-loaded. There is little waste, though 'transparent' is slightly vague and the second clause could be clearer.

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?

The tool has a complex nested observations input, no output schema, and 0% schema description coverage. The description explains high-level scope and safety but says nothing about what the analysis returns or how the required inputs are structured, leaving too much implicit for reliable invocation.

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 carry parameter meaning. It hints at aliases, citations, prompt types, and failures, but leaves brandName, brandDomain, and most nested observation fields unexplained, which is insufficient for a four-parameter tool with a complex observations array.

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 and resource ('Analyze caller-supplied answers') and clearly distinguishes the tool from model-querying siblings by stating it 'does not call models.' It also names concrete checks (brand alias and citation checks) that define the analysis scope.

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 phrase 'caller-supplied answers' and 'does not call models' tells the agent to use this tool only when answers already exist, not to generate them. It does not explicitly name edgeo_query_model as the alternative for fetching answers, so it falls short of full when/when-not/alternative guidance.

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