Postgres MCP Pro

get_object_details

Retrieve columns for a specified table or view by providing schema and object names. Use an optional database URL to override the default connection.

Instructions

Get columns of a table or view.

Args:
    schema_name: Schema containing the object.
    object_name: Table or view name.
    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>",
      "object_name": "<object_name>"
    },
    "name": "get_object_details"
  }
}

Input Schema

Name	Required	Description	Default
`object_name`	Yes
`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

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It implies a non-mutating read operation through the word 'Get', but it does not explicitly disclose read-only behavior, permission requirements, failure modes, or return characteristics.

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 minimal and well-organized: a single-sentence purpose followed by a clear argument list. There is no filler or redundant restatement of the tool name.

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 definition is adequate for a simple introspection tool: all parameters are documented and the purpose is clear. However, with no annotations and no output schema, it omits the return format (e.g., column names, types) and any permission or error behavior, so an agent must still infer some important details.

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?

The input schema has no property descriptions (0% coverage), but the description fully compensates with an Args block explaining schema_name, object_name, and database_url. It also clarifies that database_url is optional and falls back to DATABASE_URL from .env, which is valuable beyond the schema's default null.

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 uses a specific verb ('Get') and a concrete resource ('columns of a table or view'), which clearly states what the tool does. It also distinguishes the tool from sibling operations like list_objects or execute_sql by narrowing the scope to table/view columns.

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?

The description provides no guidance on when to use this tool versus its siblings, and it names no alternatives or exclusion cases. It only documents arguments, leaving the agent to infer appropriate usage context.

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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