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MCPg - Production-grade PostgreSQL MCP Server

MCPg - Production-grade PostgreSQL MCP Server

by devopam
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get database DDL

get_database_ddl
Read-only

Return the CREATE DATABASE DDL for a named PostgreSQL database using PG 19's pg_get_databasedef function, and report found=false with empty DDL when the database does not exist.

Instructions

Return the CREATE DATABASE DDL for a named database using PG 19's pg_get_databasedef(oid) function. Returns found=false with an empty ddl when the database doesn't exist. Requires PG 19+. Returns an object with object_type ('database'), object_name, found, and ddl.

Example: get_database_ddl(database_name='analytics')

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": {
      "database_name": "<database_name>"
    },
    "name": "get_database_ddl"
  }
}

Input Schema

NameRequiredDescriptionDefault
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.
database_nameYes

Output Schema

NameRequiredDescriptionDefault
ddlYes
foundYes
object_nameYes
object_typeYes

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

A3.8/5.0
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 real behavioral context beyond them: the PG 19+ version requirement and the specific not-found contract (`found=false` with an empty `ddl` instead of an error). That not-found semantics is the most decision-relevant detail and is not derivable from 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action, followed by the failure contract, the version requirement, and a concrete example — a sensible order with little waste. The return-shape sentence slightly duplicates what the output schema already provides.

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?

For a two-parameter read-only introspection tool with an output schema and annotations, the description covers the essentials: what it returns, version gating, not-found behavior, and an example call. Return value explanation is not needed given the output schema exists.

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 coverage is 50%: the `database` parameter has a rich description in the schema, but `database_name` has none there. The description and example clarify that `database_name` is a plain database name string, but it never mentions the `database` secondary-targeting parameter, 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.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: return the `CREATE DATABASE` DDL for a named database, and names the underlying `pg_get_databasedef(oid)` mechanism. It is clearly distinct from siblings like get_role_ddl and get_tablespace_ddl by resource, though it never explicitly contrasts itself with them.

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?

Usage is implied rather than stated: it says the tool needs PG 19+, which functions as a precondition, and the example shows the typical call. There is no explicit when-to-use/when-not or routing to alternatives such as get_pg19_ddl_status for capability checking.

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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