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kouko

redshift-comment-mcp

by kouko

Get Column Comment

get_column_comment

Retrieve the authoritative comment for a Redshift column to understand its business meaning and calculation logic, avoiding reliance on column names.

Instructions

Get the authoritative comment for a column — defines its business meaning and calculation logic; trust it over the column name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_nameYes
column_nameYes
schema_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior3/5

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

There are no annotations, so the description must carry the burden. It adds the useful trait that the returned comment is authoritative and should be preferred over the column name, but it does not disclose behaviors such as whether a missing comment returns null or throws, or whether permissions are needed. Given the output schema exists, this is acceptable but not fully transparent.

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 entire description is one front-loaded sentence that earns its place: it states the action, the resource, and the reason to trust the result. No filler or repetition.

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 simple three-parameter getter with an output schema, the description gives the essential context: the comment is authoritative and business-meaningful. It does not address edge cases like absent comments, but the output schema covers the return shape and the operation is straightforward.

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%, so the description needed to compensate, but it only mentions 'column' and never explains schema_name or table_name. The parameter names are self-explanatory, but the definition provides no additional semantic detail about any of the three required parameters.

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?

Description opens with 'Get the authoritative comment for a column', a specific verb-resource pairing that clearly states what the tool does. The added note that the comment defines business meaning and calculation logic distinguishes it from generic column metadata and from sibling comment tools like get_table_comment.

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?

It tells the agent when to rely on this tool: when the authoritative business meaning or calculation logic of a column is needed, and to trust it over the column name. It does not explicitly contrast with get_table_comment or get_all_column_comments, but the column-specific scope and 'trust it' guidance provide clear context.

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