Identify field mismatches between SQLAlchemy ORM models and Pydantic schemas by comparing specified files and models, revealing discrepancies that cause validation or serialization errors.
Detect field mismatches between a SQLAlchemy ORM model and a Pydantic schema.
| Name | Required | Description | Default |
|---|---|---|---|
| orm_file | Yes | ||
| orm_model | Yes | ||
| schema_file | Yes | ||
| schema_model | Yes |
Changes observed during successful MCP inspections.
v0.3.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states that the tool detects mismatches, which implies a read-only analysis, but it does not say whether files are read from disk, whether anything is modified, how results are returned, or what error conditions exist.
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?
A single, well-formed sentence with no redundant words. The core function is front-loaded ('Detect field mismatches') and the target types are specified compactly.
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 four required parameters, no output schema, no annotations, and no parameter documentation, this description is far from complete. It omits return format, file path expectations, failure modes, and any behavioral caveats, leaving an agent to guess at invocation details.
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 0%, so the description must compensate for four undocumented required parameters. The phrase 'between a SQLAlchemy ORM model and a Pydantic schema' loosely maps to orm_model and schema_model, but it does not explain the file path parameters (orm_file, schema_file), the expected model identifiers, or how the parameters interrelate.
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 a specific verb ('Detect') and a specific resource ('field mismatches between a SQLAlchemy ORM model and a Pydantic schema'). It is distinguishable by name and function from most siblings, though it does not explicitly contrast with validate_response_models or inspect_model.
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 gives no guidance on when to prefer this tool over siblings like validate_response_models or inspect_model, nor any conditions, prerequisites, or exclusions. The intended usage is only implied by the tool's purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.