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AbdessamadTzn

FastAPI Architect MCP

inspect_model

Inspect a Pydantic model to view its fields, types, defaults, and validators. Understand model structure without reading source files manually.

Instructions

Inspect a Pydantic model: fields with types/defaults, and validators.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileYes
modelYes

Implementation Reference

  • The 'inspect_model' function is registered as an MCP tool via the @mcp.tool() decorator.
    @mcp.tool()
  • The 'inspect_model' handler function that parses a Python file's AST to find a Pydantic model class, extracting its fields (with types and defaults) and validators (decorated with @validator/@field_validator). Returns a dict with model name, field list, and validator list.
    def inspect_model(file: str, model: str) -> dict:
        """Inspect a Pydantic model: fields with types/defaults, and validators."""
        tree = _parse(file)
    
        for node in ast.walk(tree):
            if not (isinstance(node, ast.ClassDef) and node.name == model):
                continue
    
            bases = [b.id for b in node.bases if isinstance(b, ast.Name)]
            if "BaseModel" not in bases:
                return {"error": f"'{model}' does not inherit from BaseModel"}
    
            fields = []
            validators = []
    
            for item in node.body:
                if isinstance(item, ast.AnnAssign) and isinstance(item.target, ast.Name):
                    fields.append({
                        "name": item.target.id,
                        "type": ast.unparse(item.annotation),
                        "default": ast.unparse(item.value) if item.value else None,
                    })
                if isinstance(item, (ast.FunctionDef, ast.AsyncFunctionDef)):
                    for dec in item.decorator_list:
                        if (
                            isinstance(dec, ast.Call)
                            and isinstance(dec.func, ast.Name)
                            and dec.func.id in ("validator", "field_validator")
                            and dec.args
                        ):
                            validators.append({
                                "function": item.name,
                                "field": dec.args[0].value if isinstance(dec.args[0], ast.Constant) else ast.unparse(dec.args[0]),
                            })
    
            return {"model": model, "fields": fields, "validators": validators}
    
        return {"error": f"Model '{model}' not found in {file}"}
  • The '_parse' helper utility used by 'inspect_model' to read and AST-parse a Python file.
    def _parse(file: str) -> ast.Module:
        with open(file) as f:
            return ast.parse(f.read())
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits such as read-only nature, side effects, authentication needs, or rate limits. The read-only behavior is implied but not stated.

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 a single sentence that front-loads the purpose. It is concise but could be slightly more informative without losing brevity.

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

Completeness2/5

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

With no output schema and two parameters, the description should explain what the tool returns (e.g., fields, validators list) and clarify parameter meaning. It fails to do so, leaving gaps in agent understanding.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not add any meaning beyond the bare parameter names. 'file' and 'model' are not explained (e.g., file path, model name), leaving the agent without guidance on their semantics.

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

Purpose5/5

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

The description clearly states the verb 'inspect' and the resource 'Pydantic model', specifying what aspects are inspected (fields with types/defaults, and validators). It distinguishes from siblings like 'list_models' or 'find_model_usages' by focusing on internal details of a specific model.

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?

The description implies usage for getting model details, but lacks explicit guidance on when to use this tool versus alternatives. No exclusions or context are provided.

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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