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run_look

Executes a saved Look query and returns the results in JSON, CSV, or plain text. Supports dev mode, branch swapping, and user impersonation.

Instructions

Run the query associated with a saved Look and return its results. Looks are pre-built query configurations saved in Looker.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
look_idYesID of the saved Look
result_formatNoOutput format: 'json', 'csv', 'txt'json
limitNoMaximum rows to return
dev_modeNoResolve the Look's model+explore against the dev workspace's LookML rather than production. Implied when ``branch`` is set.
branchNoProject branch to atomically swap to for this call (saved branch restored on exit). Requires project_id.
project_idNoLookML project ID owning the Look's model — required with ``branch``
act_as_userNoOptional Looker user ID or email to impersonate for this call. Use to operate on another user's dev workspace (Looker dev mode is per-user-isolated) or to run as a dedicated CI service user. Requires sudo capability on the configured admin credentials. When omitted, the call uses the configured or gateway-provided identity.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations, the description must disclose behavioral traits but only states 'run the query... and return its results.' It omits critical info such as read-only nature, auth requirements, or side effects. The parameter descriptions help but are not part of the main description.

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?

Two concise sentences with no wasted words. The definition is front-loaded with the core action and includes a brief explanatory note about Looks.

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 existence of an output schema and full parameter descriptions, the description is adequate but could be improved by noting that the operation is read-only or by hinting at advanced features like dev mode. It meets the minimum threshold but doesn't excel.

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?

Schema coverage is 100%, so parameters are already well-documented. The description adds no extra meaning beyond 'saved Look' implying look_id. Baseline 3 is appropriate as the description does not enhance the parameter understanding.

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 clearly states the tool runs a saved Look and returns results, using a specific verb-resource combination. It distinguishes from siblings like 'run_dashboard' and 'run_query' by focusing on 'saved Looks', but does not explicitly contrast with those alternatives.

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?

No guidance is provided on when to use this tool versus alternatives (e.g., run_query for ad-hoc queries or run_dashboard for dashboards). The description simply states what it does without contextualizing its appropriate use cases.

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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