Aliyun RDS Supabase MCP Server

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generate_typescript_types

Generate TypeScript type definitions from your database schema with the Supabase CLI, saving the output file directly to your workspace.

Instructions

Generates TypeScript types from the database schema using the Supabase CLI (supabase gen types) and saves the file inside the configured workspace. Requires DATABASE_URL configuration and Supabase CLI installed.

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": "generate_typescript_types"
  }
}

Input Schema

Name	Required	Description	Default
`output_path`	No	Path inside the workspace where the generated TypeScript file should be saved.
`output_filename`	No	Filename to save the generated types to in the workspace root.	database.types.ts
`included_schemas`	No	Database schemas to include in type generation.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.10Jul 14, 2026

TDQS

A3.7/5.0

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 describes the action and prerequisites, but does not disclose potential side effects (e.g., file overwriting), error handling behavior, or what happens if prerequisites are not met. The description is adequate but not fully transparent.

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 very concise: two sentences. The first sentence front-loads the purpose and method, and the second adds essential requirements. No redundant or unnecessary words.

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

Completeness3/5

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

The tool has no output schema, so the description should explain the return value or action outcome. It mentions 'saves the file' but does not state what the tool returns (e.g., success message, file path). For a simple generation tool, this is a minor gap. Given context signals (no output schema, no annotations), the description is incomplete in that aspect.

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?

Schema description coverage is 100%, so the input schema already documents all three parameters. The description adds no additional meaning beyond stating the file is saved in the configured workspace. It does not enrich the understanding of parameter usage or constraints.

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 'Generates' and the resource 'TypeScript types from the database schema using the Supabase CLI'. It also specifies the output action ('saves the file inside the configured workspace'). This distinguishes it from sibling tools, as none of them mention type 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 lists prerequisites ('Requires DATABASE_URL configuration and Supabase CLI installed'), but does not explicitly state when to use this tool versus alternatives. It implies usage context by describing the task, but lacks explicit guidance on exclusions or when not to use it.

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