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query

Run analytical queries against Looker's semantic model by specifying model, explore, fields, filters, and sorts. Looker generates optimized SQL and returns JSON data rows.

Instructions

Run a query using the Looker semantic model. Specify the model, explore, fields, filters, and sorts. Looker generates the optimized SQL — you never write SQL directly. Returns data rows as JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesLookML model name (e.g. 'ecommerce')
viewYesExplore/view name within the model (e.g. 'orders')
fieldsYesFields to select — use fully-qualified names (e.g. ['orders.region', 'orders.total_revenue'])
filtersNoFilter expressions as field:value pairs (e.g. {'orders.created_date': '90 days'})
sortsNoSort expressions (e.g. ['orders.total_revenue desc'])
limitNoMaximum rows to return
result_formatNoOutput format: 'json' (default), 'json_detail', 'csv', 'txt'json
dev_modeNoRun against the dev workspace's currently-checked-out LookML rather than production. Required when validating in-progress branch edits. Implied automatically when ``branch`` is set.
branchNoProject branch to atomically swap to for this call. The dev workspace's saved branch is restored when the call completes (success or failure). Implies dev_mode=True; requires project_id.
project_idNoLookML project ID — required when ``branch`` is set so the MCP knows which project's branch state to swap.
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
Behavior3/5

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

No annotations provided, so description must fully disclose behavior. It mentions SQL generation and JSON output, but omits details on error handling, pagination, or rate limits. Adequate but could be more 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?

Two sentences, no fluff. First sentence states purpose, second elaborates. Every word earns its place. Exceptionally concise.

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?

Despite output schema existing, the tool has 11 parameters with nuanced interactions (dev_mode, branch, act_as_user). The description does not cover these complexities, leaving agent underinformed.

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% with descriptions for each parameter. The description summarizes main parameters but does not add significant new meaning or clarify interdependencies (e.g., dev_mode and branch). Baseline 3 is appropriate.

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 runs a query using the Looker semantic model, specifying model, explore, fields, filters, and sorts. It distinguishes from raw SQL writing and mentions returning JSON, making purpose very clear.

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 semantic model queries without writing SQL, but lacks explicit when-not-to-use or alternatives (e.g., query_sql for raw SQL). Guidance is present but not comprehensive.

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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