list_data_models

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

Annotations provide readOnlyHint=true, which the description doesn't contradict. The description adds valuable behavioral context beyond annotations: it discloses pagination behavior ('Returns paginated list'), specifies what metadata is included ('mappings and log types'), and mentions the permission requirement ('Permissions: {"all_of": ["View Rules"]}'). This provides useful operational context that annotations alone don't cover.

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 with three focused paragraphs: purpose statement, additional context about data models, and behavioral details. Each sentence adds value without redundancy. The structure is logical with purpose first, though the permissions information might be better integrated rather than appended.

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 relative simplicity (list operation with 2 optional parameters), 100% schema coverage, readOnlyHint annotation, and existence of an output schema, the description provides excellent contextual completeness. It covers purpose, behavioral traits (pagination, metadata, permissions), and context about what data models are, making it fully adequate for agent understanding.

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%, with both parameters (cursor, limit) well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, but doesn't need to since schema coverage is complete. The baseline score of 3 is appropriate when the schema carries the full parameter documentation burden.

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 specific action ('List all data models') and resource ('from your Panther instance'), with additional context about what data models are used for ('used only in Panther's Python rules to map log type schema fields to a unified data model'). It distinguishes from sibling tools like 'get_data_model' by specifying it lists all models rather than retrieving a single one.

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 usage context by mentioning data models are used in Panther's Python rules, but doesn't explicitly state when to use this tool versus alternatives like 'get_data_model' or other list tools. It includes permissions information, which provides some guidance on prerequisites, but lacks explicit when/when-not instructions or named alternatives.

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