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BigQuery MCP

BigQuery MCP

by deBilla
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

list_scheduled_queries

Read-onlyIdempotent

View scheduled queries to see what they write, when they run, and their state. Identify disabled or failing queries that cause table staleness.

Instructions

List scheduled queries: what they write, when they run, and their state.

Use this to answer "what populates this table?" and "why is this table stale?" — a disabled or failing scheduled query is the usual cause, and check_table_freshness can see the staleness but not the reason.

The SQL is not included here; call get_scheduled_query for one of them.

Args: dataset: Only queries writing into this destination dataset. contains: Only queries whose name contains this text. include_disabled: Keep disabled queries in the result. They are the most likely explanation for a table that stopped updating, so this defaults to True. environment: Which configured environment to look in. Scheduled queries are regional, so this must be the environment whose location holds them.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_scheduled_queries"
  }
}

Input Schema

NameRequiredDescriptionDefault
datasetNo
containsNo
environmentNo
include_disabledNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so safety is covered. The description adds real context beyond them: the SQL is deliberately excluded, disabled queries are retained because they are the likeliest cause of stalled tables, and scheduled queries are regional so environment must match. Return format and pagination are not described, keeping it just short of a 5.

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 lead sentence and the usage paragraph are front-loaded and dense with decision-relevant information. The Args block is longer than strictly necessary for some entries, but each line adds semantics absent from the schema, so there is little filler to cut.

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?

With no output schema and no enum metadata, the description compensates well by summarizing the shape of the result (write target, schedule, state) and by pointing to get_scheduled_query for the detail it omits. It stops short of describing pagination or result limits, which an agent listing potentially many queries would benefit from knowing.

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?

Schema description coverage is 0%, so the description carries the entire burden and does so for all four parameters. It clarifies that dataset filters on the destination dataset (not the source), that contains matches on query name, that environment is a location constraint driven by regionality, and it explains both the value and default of include_disabled rather than merely restating it.

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?

States a specific verb and resource ('List scheduled queries') and immediately defines the scope of returned data ('what they write, when they run, and their state'). It also separates itself from get_scheduled_query, which is where the SQL lives, so an agent can distinguish the two without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit use cases in the user's own words ('what populates this table?', 'why is this table stale?') and names the alternative tool check_table_freshness with the reason it is insufficient ('can see the staleness but not the reason'). It also issues a clear redirect: call get_scheduled_query when the SQL itself is needed.

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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