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create_named_query

Save a SQL query as a reusable named query in AWS Athena. Specify the query name, database, SQL string, optional work group, and description to create it.

Instructions

Create a named query

Args: name: Name of the query database: Database for the query query_string: SQL query string description: Description of the query work_group: Work group for the query

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
databaseYes
work_groupNo
descriptionNo
query_stringYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only restates the create action and parameter labels, without explaining persistence, duplicate-name behavior, permissions, or what happens if work_group is omitted. The verb implies mutation but adds no useful context beyond it.

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?

The description is short, front-loaded with the action, and the Args list is scannable. However, the parameter list largely duplicates information already present in the schema, so some of it is redundant rather than informative.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a creation tool with three required parameters, no annotations, and no schema descriptions, this description is incomplete. It does not explain where the named query is stored, whether creating a duplicate name overwrites or fails, or what work_group/database values are valid. The presence of an output schema covers return values, but not these important operational details.

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 must compensate. It lists all five parameters, but most entries merely paraphrase the schema titles ('name: Name of the query', 'database: Database for the query'). Only 'query_string: SQL query string' adds substantive meaning. No constraints, formats, or dependencies are described.

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 opening line 'Create a named query' specifies the exact verb and resource, making the tool's core action immediately clear. This also distinguishes it from sibling tools like list_named_queries, get_named_query, and delete_named_query, which use different verbs.

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 gives no guidance on when to use this tool versus alternatives such as start_query_execution or list_named_queries. It also fails to mention prerequisites, like whether the database or work_group must already exist.

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