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find_pydantic_models

Locate all Pydantic models in a Python project and optionally limit the search to the main project, all configured namespaces, or a specific namespace.

Instructions

Find all Pydantic models in the project.

Args: scope: Search scope (default: this tool's scope_defaults entry in the project config, else scope_defaults.global, else "main"): - "main": Only the main project (default for plugins) - "all": Include configured namespaces - "namespace:name": Specific namespace

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "find_pydantic_models"
  }
}

Input Schema

Name	Required	Description	Default
`scope`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv2.0.0Nov 7, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are supplied, so the description carries the burden. 'Find' implies a read-only query and the config-resolution default chain for scope is genuinely useful behavioral context, but nothing is said about result shape, ordering, or whether models from dependencies are included. Safety risk is low, so the gap is moderate rather than severe.

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?

One front-loaded purpose sentence followed by structured Args documentation for the single parameter. The default-resolution sentence is long but informative rather than filler; nothing is redundant.

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?

An output schema exists, so return values need no explanation. For a one-parameter discovery tool, the description covers purpose and scope semantics adequately; only the multi-scope array input and the treatment of transitively imported models are left open.

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 description coverage is 0%, yet the description compensates well: it enumerates the accepted scope values ('main', 'all', 'namespace:name') and explains the full default resolution chain. The one gap is the array form allowed by the anyOf schema, which is never mentioned or explained.

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 opens with a specific verb and resource: 'Find all Pydantic models in the project.' That is unambiguous and separates it from siblings like find_validators or find_model_config. It stops short of explicitly contrasting with get_model_schema, which returns schema details for a single model rather than listing models.

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

Usage Guidelines3/5

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

Usage is implied by the verb 'Find' plus the scope escalation semantics (main -> all -> namespace:name), which tells an agent how to broaden a search. There is no explicit when-to-use or when-not-to-use guidance against the many sibling find_* tools, so the agent must infer the boundary.

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