list_data_models

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

No annotations are provided, so this description carries the burden of behavioral disclosure. It specifies the return format as 'A markdown-formatted list' and mentions the ctx parameter for logging and error handling. While it does not explicitly state that the tool is read-only, the verb 'list' inherently implies a non-destructive operation, and the description adds useful detail about the output format.

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 concise but has minor redundancy: the first sentence 'List all available data models.' is repeated in the second sentence 'This tool lists all available data models.' However, the second sentence adds value by specifying the relationship with get_data_model_types. The structure is front-loaded with the main purpose, and the args/returns sections are clearly separated, making it easy to scan.

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 parameters and a simple listing function, the description is complete. It explains what the tool returns, mentions its relationship to a sibling tool, and includes the ctx parameter for logging. Since an output schema exists, the description need not detail the return structure further. All essential information is provided.

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?

Since the input schema has zero parameters, the baseline is 4. The description mentions a 'ctx' argument for MCP context, which is not present in the schema. While this could cause slight confusion, it is likely an auto-injected parameter and the description helps explain its purpose. The tool is simple enough that no further parameter documentation is needed.

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 function with a specific verb and resource: 'List all available data models.' It also distinguishes itself from related tools by noting these models 'can be used with get_data_model_types,' which is a sibling tool. This makes the purpose unambiguous and differentiates it from list_data_stores and other list 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 clear context by linking the tool to get_data_model_types, implying it is a precursor for that tool. However, it does not explicitly mention when not to use it or recommend alternative tools for different scenarios. Since it gives a clear usage context without explicit exclusions, a score of 4 is appropriate.

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