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kouko

redshift-comment-mcp

by kouko

Get Table Comment

get_table_comment

Retrieve the authoritative comment for a Redshift table to understand what data it contains, using schema and table names. Trust this comment over the table name for accurate data discovery.

Instructions

Get the authoritative comment for a table — defines what data it actually contains; trust it over the table name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYes
schema_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral disclosure burden. It communicates that the result is authoritative and should be trusted over the table name, which is useful behavioral context. It does not address potential empty comments, error conditions, or permissions, but the output schema covers return structure.

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?

A single, front-loaded sentence that states the operation and adds a meaningful directive ('trust it over the table name'). No filler or repetitive material.

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?

For a simple two-parameter read tool with an output schema, the description is nearly sufficient. The main missing pieces are explicit parameter semantics and guidance on alternatives, which keeps it from being fully complete for an agent deciding among the comment and search siblings.

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

Parameters2/5

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

Schema description coverage is 0% and the description does not explain schema_name or table_name beyond what their property names already imply. Since the description was expected to compensate for the missing schema descriptions, this is a gap even though the parameter names are self-explanatory.

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 description states a specific action and resource ('Get ... comment for a table') and adds meaning by calling the comment 'authoritative' and contrasting it with the table name. It does not explicitly differentiate from sibling comment tools like get_column_comment or get_schema_comment, though the table focus is implied by the name.

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?

It implies when to use the tool: when you need the actual meaning of a table rather than guessing from its name ('defines what data it actually contains; trust it over the table name'). No explicit exclusions or comparisons to alternatives such as get_all_column_comments or get_schema_comment are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.