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okeefeco

PyEye Server

by okeefeco

trace_model_inheritance

Trace the inheritance hierarchy of any Pydantic model by specifying the model name and optional search scope.

Instructions

Trace the inheritance hierarchy of a Pydantic model.

Args: model_name: Name of the model to trace scope: Search scope (default "main"): - "main": Only the main project (default for plugins) - "all": Include configured namespaces - "namespace:name": Specific namespace

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_nameYes
scopeNomain

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations exist, so the description carries the behavioral burden. It implies a read-only operation (tracing) without declaring side effects, permissions, or rate limits. This is neutral but insufficient to score higher; no contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is relatively short and structured with an Args block. However, the header 'Trace the inheritance hierarchy of a Pydantic model.' is somewhat redundant with the tool's name, and the docstring format could be tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (not shown but signaled), the description adequately covers the input parameters and basic purpose. It does not explain return value details, but the output schema likely handles that. For a single-purpose tool, this is sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, meaning the description must compensate. It explains both parameters, especially detailing the scope values ('main', 'all', 'namespace:name'). However, it omits that scope can also be an array of strings, which limits completeness.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Trace' and identifies the resource 'inheritance hierarchy of a Pydantic model', making the purpose clear. While it distinguishes from siblings like 'trace' (more generic) and 'get_model_schema' (schema vs hierarchy), it does not explicitly highlight the uniqueness, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description only explains the parameters, not the context or when-not-to-use. Sibling tools like 'find_models' or 'trace' are not mentioned, leaving the agent to infer usage.

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

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