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Schema Bridge MCP Server

Schema Bridge MCP Server

by rafim-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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schema_generate_mock

Generate realistic synthetic mock JSON data from SQL, Prisma, or JSON schemas. Produce relational records with names, emails, dates, UUIDs, currencies, and statuses for testing.

Instructions

Generate realistic, relational synthetic mock JSON data (names, emails, dates, UUIDs, currencies, statuses) from any SQL, Prisma, or JSON schema.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "schemaInput": "<schemaInput>"
    },
    "name": "schema_generate_mock"
  }
}

Input Schema

NameRequiredDescriptionDefault
countNoNumber of mock records to generate (default: 3, max: 50).
entityNameNoModel name if not specified in schema.
schemaInputYesRaw SQL DDL, Prisma model definition, or JSON string.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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 clarifies the output (mock JSON data) but does not mention any side effects, permissions, or whether the operation is read-only. The description gives a functional overview without explicit behavioral details.

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?

The description is a single, concise sentence that includes the core purpose and example data types. It is well-structured and front-loaded with the main action, making it easy to parse.

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 complete for the tool's purpose, covering input formats and output type. It mentions 'relational' to hint at relationship handling, but does not elaborate on output structure. Given the lack of an output schema, this is sufficient but not overly detailed.

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

Parameters5/5

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

All three parameters are described with sufficient detail: count specifies range and default, entityName clarifies optionality, and schemaInput explains the accepted formats. Coverage is 100% and the descriptions add meaningful context.

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 tool's function: generating realistic, relational synthetic mock JSON data. It identifies the input resource (SQL, Prisma, or JSON schema) and distinguishes it from sibling tools like schema_validate and schema_convert by focusing on generation.

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 implicitly indicates use when mock data is needed from a schema, but it does not explicitly contrast with when to use schema_validate or schema_convert. No direct 'use this for X, not for Y' guidance is 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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