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query_history_list

Read-only

List historical SQL query executions in Databricks, filtering by time range, user, status, or warehouse. Retrieve performance metrics and paginate results.

Instructions

List historical SQL query executions with optional filters.

Builds the ``filter_by`` payload for the Databricks query history API
from the supplied keyword arguments. ``include_metrics`` and pagination
parameters are passed as top-level query string parameters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_time_msNoFilter start: epoch ms (inclusive)
end_time_msNoFilter end: epoch ms (inclusive)
user_idsNo
statementsNo
statusesNoQUEUED | RUNNING | CANCELED | FAILED | FINISHED
warehouse_idsNo
endpoint_idsNo
max_statement_lengthNoTruncate statement text to this many chars
max_files_per_queryNoLimit on number of files returned per query
include_metricsNoInclude performance metrics
page_tokenNo
page_sizeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

The description confirms the read-only nature (consistent with readOnlyHint annotation) and explains the technical construction of the API call. However, it does not disclose rate limits, authentication requirements, or behavior with large result sets beyond pagination hints. The added technical detail marginally improves transparency beyond annotations.

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 three sentences, each adding unique value: the first states the purpose, the second explains the payload construction, and the third clarifies parameter passing. No redundant or unnecessary text.

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?

Given the tool's 12 parameters, an output schema, and readOnly annotation, the description is technically adequate but lacks broader usage context. It does not situate the tool among related query tools or explain typical use cases.

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

Parameters3/5

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

With 50% schema description coverage, the description adds summary context by stating parameters are passed as filter_by payload or top-level, and mentions optional filters. However, it does not individually describe the 6 undocumented parameters or compensate for their lack of schema descriptions.

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 lists historical SQL query executions with optional filters, and explains that it builds the filter_by payload from supplied arguments and that include_metrics and pagination are top-level parameters. This differentiates it from other list tools in the sibling set, none of which are query-history-specific.

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?

The description implies usage for listing historical query executions but does not explicitly state when to use this tool over alternatives (e.g., sql_statements_execute for live queries) or provide exclusion criteria. No guidance on prerequisites or typical scenarios.

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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