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rafim-dev

Schema Bridge MCP Server

schema_convert

Convert SQL DDL, Prisma schemas, or JSON payloads into Zod schemas, TypeScript interfaces, or Pydantic v2 models.

Instructions

Convert any database schema (SQL DDL, Prisma) or JSON payload into production-ready Zod schemas, TypeScript interfaces, or Python Pydantic v2 models.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityNameNoName of the entity/model (default: inferred from schema).
schemaInputYesRaw SQL DDL, Prisma model definition, JSON string, or file path.
targetFormatYesDesired output code format.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.8/5.0
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 any behavioral details such as side effects, error handling, whether file paths are read, or if the operation is pure. The phrase 'production-ready' implies quality but does not explain actual behavior.

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

Conciseness5/5

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

A single, clear sentence that conveys the full purpose without redundancy. All key elements (input, output, and scope) are included efficiently.

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?

The description is adequate for a conversion tool: it specifies the input and output formats and implies the scope. Given no output schema is present, no return details are needed. While it could mention edge cases or format-specific notes, the description is complete for typical usage.

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

Parameters3/5

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

The schema provides 100% coverage of parameters (schemaInput, targetFormat, entityName) with descriptions. The tool description itself does not add extra meaning beyond the schema, so it meets the baseline but does not exceed it.

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 'convert' and specifies the input (database schema or JSON payload) and output (Zod, TypeScript interfaces, Pydantic v2 models). It is distinct from sibling tools like schema_validate and schema_generate_mock, which serve different purposes.

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

Usage Guidelines4/5

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

The purpose inherently implies when to use (when conversion between schema formats is needed). While not explicitly stating 'use this instead of validate or mock generation', the sibling contexts make the usage clear. No explicit alternative conditions are provided, but the distinction is obvious.

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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