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handle_check_cluster_health

Assess the health of Redshift clusters by executing diagnostic SQL scripts. Choose 'basic' for operational status or 'full' for detailed table design checks. Returns results or errors in a structured dictionary for analysis.

Instructions

Performs a health assessment of the Redshift cluster.

Executes a series of diagnostic SQL scripts concurrently based on the
specified level ('basic' or 'full'). Aggregates raw results or errors
from each script into a dictionary.

Args:
    ctx: The MCP context object.
    level: Level of detail: 'basic' for operational status, 'full' for
           comprehensive table design/maintenance checks. Defaults to 'basic'.
    time_window_days: Lookback period in days for time-sensitive checks
                      (e.g., queue waits, commit waits). Defaults to 1.

Returns:
    A dictionary where keys are script names and values are either the raw
    list of dictionary results from the SQL query or an Exception object
    if that specific script failed.

Raises:
    DataApiError: If a critical error occurs during script execution that
                  prevents gathering results (e.g., config error). Individual
                  script errors are captured within the returned dictionary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
levelNobasic
time_window_daysNo
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: concurrent execution of scripts, aggregation of results into a dictionary, error handling approach (individual script errors captured in dictionary vs. critical errors raised as DataApiError), and the distinction between basic and full diagnostic levels.

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 well-structured with clear sections (purpose, execution behavior, args, returns, raises) and front-loaded with the core purpose. While comprehensive, some sentences could be more concise, such as the detailed explanation of the return dictionary which is slightly verbose.

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 tool with no annotations, no output schema, and 0% schema description coverage, the description provides substantial context including purpose, parameters, return format, and error handling. However, it doesn't mention authentication requirements, rate limits, or potential side effects on the cluster, which would be helpful given the diagnostic nature.

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 schema has 0% description coverage, so the description fully compensates by providing detailed semantic explanations for both parameters: 'level' options ('basic' for operational status, 'full' for comprehensive checks) and 'time_window_days' purpose (lookback period for time-sensitive checks like queue waits). It also mentions default values and provides concrete examples.

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 clearly states the tool 'performs a health assessment of the Redshift cluster' with specific verbs ('executes diagnostic SQL scripts', 'aggregates results') and distinguishes it from siblings by focusing on comprehensive cluster health rather than specific issues like locks, query performance, or table inspection.

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?

The description provides clear context about when to use different levels ('basic' for operational status, 'full' for comprehensive checks) and mentions time-sensitive checks, but doesn't explicitly state when to choose this tool over sibling tools like handle_diagnose_query_performance or handle_monitor_workload for similar health-related tasks.

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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