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hungln105-cell

notebooklm-mcp-cli

Pipeline

pipeline

Run or list multi-step notebook pipelines to automate workflows like ingest-and-podcast on target notebooks.

Instructions

Manage and execute multi-step notebook pipelines.

Actions:

  • run: Execute a pipeline on a notebook

  • list: List all available pipelines (builtin and user-defined)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYesOperation to perform (run, list)
input_urlNoURL variable for pipelines that need it (replaces $INPUT_URL)
notebook_idNoTarget notebook UUID (required for action=run)
pipeline_nameNoPipeline name (required for action=run, e.g. "ingest-and-podcast")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.11.5

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. 'Execute' implies that run triggers meaningful work, but the description does not state whether execution is asynchronous, long-running, destructive, or what side effects it may have on the notebook or related resources. Only 'list' is obviously safe by inspection.

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 compact, front-loaded with the resource, and uses a scannable bullet list for the two actions. Every line earns its place, with no redundant elaboration or filler.

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?

For a two-action dispatcher, the description sufficiently covers the tool's purpose while the schema covers parameter requirements and an output schema exists for return values. The main gap is behavioral context around execution side effects, but this is a minor omission for basic invocation.

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%, and each parameter already has a meaningful description, including required-for-run semantics and the $INPUT_URL substitution behavior. The tool description adds no parameter-level detail beyond naming the run/list actions, so the schema carries the interpretive weight; baseline 3 applies.

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 identifies the resource ('multi-step notebook pipelines') and enumerates two concrete operations, run and list, each with a one-line definition. It is specific enough to convey what the tool does, though it does not explicitly differentiate this tool from sibling tools such as batch or notebook_query.

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 action list provides clear context for selecting between run and list: run to execute a pipeline on a notebook, list to enumerate available pipelines. However, it gives no guidance on when to prefer this tool over alternative sibling tools, and prerequisites are left to the schema rather than stated in the description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.