Postgres MCP Pro

list_objects

List tables, views, or sequences in a specified PostgreSQL schema, enabling direct inspection of database object structure.

Instructions

List tables/views/sequences in a schema.

Args:
    schema_name: Name of the schema to list objects from.
    object_type: One of "table", "view", "sequence" (default: "table").
    database_url: Database URL (optional, uses DATABASE_URL from .env if omitted).

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_name": "<schema_name>"
    },
    "name": "list_objects"
  }
}

Input Schema

Name	Required	Default
`object_type`	No	table
`schema_name`	Yes
`database_url`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.4.2Sep 6, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description carries the behavioral burden. 'List' implies a read-only operation, and the note about database_url defaulting to DATABASE_URL from .env adds useful behavioral context. However, it does not describe return format, error behavior, or any permissions/requirements.

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 compact and front-loaded with the purpose, followed by a short parameter list. Every sentence adds value, and the formatting is easy to scan.

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 parameter semantics are well covered, and the description is sufficient for making a basic call. However, with no output schema and no annotations, the lack of any mention of return values or additional behavioral outcomes leaves a notable gap for an agent deciding whether this tool fully satisfies a request.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must explain all parameters, and it does. It clarifies schema_name, enumerates object_type options with a default, and explains database_url's optionality and environment fallback, going well beyond the bare schema.

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?

The first sentence clearly names the action (list) and resource (tables/views/sequences in a schema), making the tool's core function obvious. It is distinct from siblings like list_schemas, but it does not explicitly call out sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to prefer this tool over alternatives such as get_object_details or execute_sql. The description states what it does but not the conditions or use cases that should trigger selection.

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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