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MCPg - Production-grade PostgreSQL MCP Server

MCPg - Production-grade PostgreSQL MCP Server

by devopam
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Generate Ecto schemas

generate_ecto_schemas
Read-only

Converts a PostgreSQL schema into Ecto (Elixir) schema modules, one .ex file per table with fields, belongs_to foreign keys, and timestamps, returning filenames and source code for the agent to write.

Instructions

Read a PostgreSQL schema and emit Ecto (Elixir) schema modules — one .ex file per table, named after the singularised table. Each module uses Ecto.Schema with field declarations, belongs_to for single-column intra-schema FKs, and timestamps() when both inserted_at and updated_at exist. The Elixir top-level module is configurable via app_module (default MyApp). Returns a JSON object {filename: source} so the agent can write each file. 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.

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": {
      "schema": "<schema>"
    },
    "name": "generate_ecto_schemas"
  }
}

Input Schema

NameRequiredDescriptionDefault
schemaYes
databaseNoOptional: a database id from list_databases, to target a configured secondary (read-only) database; omit for the primary. Database names that are not configured ids are rejected.
app_moduleNoMyApp

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.8.4
    • changedInput schema / properties / database / description
      Previous value: -"Optional: target a configured secondary (read-only) database by name; omit for the primary. Call list_databases to see the configured ids."New value: +"Optional: a database id from list_databases, to target a configured secondary (read-only) database; omit for the primary. Database names that are not configured ids are rejected."
  2. First observedv0.6.8

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, but the description adds material context they do not cover: the tool does not write files itself, it returns a {filename: source} JSON map so the agent must perform the writes. It also discloses the identifier-policy constraint that causes failures. It does not discuss cost, limits on large schemas, or what happens on unsupported column types.

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?

Front-loaded with the verb and product, followed by generation rules, return contract, and the identifier restriction in descending importance. Dense but every clause carries information; the reference to docs/identifier-policy.md and the parenthetical examples are the only slightly expendable elements.

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?

For a read-only code generator with an output schema present and annotations covering safety, the description supplies the important extras: naming convention, FK and timestamp handling, module naming, the return map contract, secondary-database targeting, and the identifier limitation. Remaining gaps (behaviour on unsupported types, performance on very large schemas) are minor rather than blocking.

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 only 33%, and the description covers the two undocumented parameters: app_module is explained as the configurable Elixir top-level module with default MyApp, and schema is implied as the PostgreSQL schema to read. The database parameter is already fully documented in the schema. However, the description never clarifies what values `schema` accepts (e.g., a name from list_schemas) or what happens if it does not exist, so it does not fully compensate for the coverage 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 and resource ('Read a PostgreSQL schema and emit Ecto (Elixir) schema modules'), plus concrete output shape (one .ex file per table, named after the singularised table). This clearly separates it from the many sibling generators (generate_prisma_schema, generate_drizzle_schema, generate_ent_schemas, generate_sqlalchemy_models) without the agent needing to open any 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 plus named alternatives: objects with delimited names (hyphens, spaces, mixed-case) should go to SQL tools export_table or dump_database instead. It also explains the reason, which helps the agent generalise. What is missing is a positive trigger describing when the agent should prefer this over the other ORM generators, though the language/target in the purpose makes that largely self-evident.

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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