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MCPg - Production-grade PostgreSQL MCP Server

Generate Diesel schema

generate_diesel_schema
Read-only

Reads a PostgreSQL schema and emits a Diesel ORM schema.rs with table! macros, Nullable for nullable columns, joinable! declarations, and Text-backed enum type wrappers.

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.

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
Behavior5/5

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

Annotations declare readOnlyHint=true and description states 'Read a PostgreSQL schema', consistent with read-only behavior. The description adds behavioral details: what is emitted (macros, enum module), the limitation ('Composite FKs are a documented v1 gap'), and enum handling, which are beyond the annotations.

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 paragraph of four sentences, front-loaded with the core purpose, then efficient details on output structure, enum handling, and a known gap. No unnecessary words.

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?

Given the tool's complexity (code generation), the description covers the main purpose, output specifics, enum handling, and a known limitation. It assumes the tool reads from the live database via the schema name parameter, which is clear. Minor gap: no explicit mention of the return format, but the presence of an output schema likely covers that.

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 parameter has a description). The main parameter 'schema' lacks a description in the input schema, but the description states 'Read a PostgreSQL schema', implicitly indicating it refers to a schema name. No additional param-specific semantics are provided beyond what the schema offers.

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 'Read' and resource 'PostgreSQL schema' to 'emit a Diesel ORM (Rust) schema.rs'. It details output components (table! macro, Nullable<T>, joinable!, allow_tables_to_appear_in_same_query!, enum handling), distinguishing it from sibling ORM generation tools.

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 description explicitly identifies the tool as Diesel ORM specific ('Diesel ORM (Rust) schema.rs'), which strongly implies its use for Rust/Diesel projects. It does not explicitly state when not to use it, but the differentiation from siblings is clear from the name and content.

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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