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MCPg - Production-grade PostgreSQL MCP Server

Generate Diesel schema

generate_diesel_schema
Read-only

Read a PostgreSQL schema and emit a Diesel ORM (Rust) schema.rs. One table! macro per table with column SQL types, Nullable<T> for nullable columns, plus joinable! declarations for single-column intra-schema FKs and an…

Instructions

Read a PostgreSQL schema and emit a Diesel ORM (Rust) schema.rs. One table! macro per table with column SQL types, Nullable<T> for nullable columns, plus joinable! declarations for single-column intra-schema FKs and an allow_tables_to_appear_in_same_query! macro so multi-table joins type-check. Enum types are emitted as Text-backed wrapper enums in a pg_enum module so the output works without diesel_derive_enum. Composite FKs are a documented v1 gap. Schema/table/column names must be plain identifiers ([A-Za-z_][A-Za-z0-9_]*); delimited names (hyphens, spaces, mixed-case-only) are not supported here because they become source identifiers in generated code — use SQL tools (export_table, dump_database) for those objects. See docs/identifier-policy.md.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
schemaYes
databaseNoOptional: target a configured secondary (read-only) database by name; omit for the primary. Call list_databases to see the configured ids.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.8

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already establish readOnlyHint=true and openWorldHint=false, and the description adds substantial behavioral detail beyond them: the exact macros emitted, Nullable<T> handling, joinable! scope, Text-backed pg_enum wrappers, and the composite-FK v1 gap. It also discloses the identifier constraint and why it exists, which is real operational context.

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 purpose is front-loaded in the first sentence, and the long middle section is dense with distinct, non-redundant generation facts rather than filler. It is on the verbose side but nearly every clause carries information an agent needs.

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

Completeness5/5

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

With an output schema present, return values need not be described, and the entry covers generation semantics, enum strategy, known limitations, identifier restrictions, and fallback tools. Nothing material about calling this tool correctly is missing.

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 50%: 'database' is well documented with a pointer to list_databases, but the required 'schema' parameter has no description. The prose implies a PostgreSQL schema name but never confirms it or explains the expected format, so it only partially compensates for the gap.

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?

States a specific verb+resource: 'Read a PostgreSQL schema and emit a Diesel ORM (Rust) schema.rs'. The Diesel/Rust specificity implicitly distinguishes it from sibling generators like generate_prisma_schema or generate_drizzle_schema. An agent can identify the artifact produced without opening the schema.

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?

Gives an explicit when-not and routes to alternatives: delimited identifiers must go to export_table or dump_database instead. It does not, however, broadly frame when to choose this over the other ORM generators in the sibling set, leaving that to name inference.

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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