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BigQuery MCP

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Python
Hybrid

list_notebook_schedules

Read-onlyIdempotent

List scheduled Colab notebooks with pass/fail run counts from execution jobs to reveal true health, since schedule status alone can mislead.

Instructions

List scheduled Colab notebooks with how many recent runs passed or failed.

This is the health overview for scheduled notebook work: what is scheduled, whether it is active or paused, and — the part that is otherwise invisible — how its actual runs have been going.

Do not read a schedule's own state as health. A schedule reports its last scheduled run as "OK" when it successfully launched the notebook, whether or not the notebook then failed; on this platform every schedule says OK while hundreds of runs have failed. The pass/fail numbers here come from the execution jobs, which is the only place the outcome exists.

Args: environment: Which configured environment to read. Omit for the default. state: Restrict to 'active' or 'paused'. A paused schedule that used to fail is a common find — someone paused it instead of fixing it. name_contains: Case-insensitive substring match on the schedule name. lookback_days: How far back to read runs for the pass/fail counts. Defaults to 30 so monthly schedules show at least one run. Larger windows cost proportionally more (90 days is roughly 23 API pages). Set to 0 to skip run history entirely and just list what exists.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNo
environmentNo
lookback_daysNo
name_containsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations cover readOnly/idempotent/openWorld, so the bar is lower, yet the description adds genuinely new behavioral context: the platform quirk that every schedule reports OK regardless of run failure, where pass/fail data actually comes from (execution jobs), and the cost model of lookback_days (90 days ≈ 23 API pages).

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?

Front-loaded with purpose, and the Args block is efficient. The middle paragraph repeats the 'schedule state isn't health' point across two sentences, which is a minor redundancy but justified given how counterintuitive the caveat is.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must convey returns, and it does — list of schedules with active/paused state and pass/fail counts. Combined with full parameter documentation and the semantic caveat, an agent has everything needed to call and interpret this correctly.

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 coverage is 0% and none of the four parameters have enums or descriptions in the schema, so the description carries the full burden — and it does: environment default behavior, state values 'active'/'paused' with an interpretation tip, name_contains matching semantics, and lookback_days default rationale plus the 0-to-skip behavior.

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 Colab notebooks') plus the distinguishing payload ('how many recent runs passed or failed'). This separates it from get_notebook_schedule (single schedule detail) and list_notebook_runs (raw run listing) without the agent needing to open any schema.

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

Usage Guidelines4/5

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

Frames the tool as 'the health overview' and explicitly warns against the common misinterpretation (schedule self-reported 'OK'). It reads as clear when-to-use context but does not name sibling alternatives or state explicit exclusions by tool name.

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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