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mcp-bigquery-dryrun

by caron14

bq_dry_run_sql

Validate BigQuery SQL syntax and estimate query cost without execution. Returns cost estimates, referenced tables, and schema previews.

Instructions

Perform a dry-run of a BigQuery SQL query to get cost estimates and metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesThe SQL query to dry-run
paramsNoOptional query parameters (key-value pairs)
pricePerTiBNoPrice per TiB for cost estimation (defaults to env var or 5.0)
Behavior2/5

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It only states the tool performs a dry-run and returns cost estimates and metadata, but fails to mention key aspects such as idempotency (safe to call multiple times), required permissions, rate limits, or what exactly 'metadata' includes. The non-destructive nature is implied but not explicit.

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 a single sentence that efficiently conveys the tool's purpose. It is front-loaded and contains no unnecessary words or information, earning its place.

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?

For a tool with 3 parameters (one nested object) and no output schema, the description is minimal. It outlines the goal but omits details on return values, error handling, and edge cases. While the tool is straightforward, the lack of output schema means the description should provide more context on what the agent can expect from the dry-run results.

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 description coverage is 100%, with each parameter described in the input schema. The description itself adds no additional semantic meaning beyond what the schema provides. Baseline is 3 because the schema does the heavy lifting; the description does not compensate for any gaps.

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 performs a dry-run of a BigQuery SQL query to get cost estimates and metadata. It uses a specific verb (dry-run) and resource (BigQuery SQL query). However, it does not distinguish from the sibling tool 'bq_validate_sql', which may also involve checking queries, reducing clarity on when to use each.

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 provides no guidance on when to use this tool versus alternatives like bq_validate_sql. There is no mention of prerequisites, context, or situations where this tool is appropriate or not, leaving the agent with no decision support.

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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