Skip to main content
Glama

pipeline

Execute multi-step pipelines on notebooks to automate content processing tasks, such as ingestion and podcast generation.

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)

Args: action: Operation to perform (run, list) notebook_id: Target notebook UUID (required for action=run) pipeline_name: Pipeline name (required for action=run, e.g. "ingest-and-podcast") input_url: URL variable for pipelines that need it (replaces $INPUT_URL)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
notebook_idNo
pipeline_nameNo
input_urlNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations provided. Description only mentions actions and arguments but does not disclose behavioral traits like idempotency, side effects, error handling, or whether run/list are synchronous or asynchronous. The existence of an output schema partially compensates, but description lacks crucial operational context.

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?

Description is brief and well-structured: a one-line purpose, bulleted action list, and parameter table. Every sentence adds value with no redundancy.

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?

Given that an output schema exists (covering return values) and the description covers all parameters and actions, the tool is adequately described for basic usage. Minor gap in behavioral transparency prevents a perfect score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% parameter descriptions; description fully explains each parameter (e.g., 'action: Operation to perform (run, list)', 'notebook_id: Target notebook UUID (required for action=run)'). Adds meaning beyond raw schema types.

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?

Description clearly states verb 'Manage and execute' with resource 'multi-step notebook pipelines' and explicitly lists two actions ('run' and 'list'), distinguishing it from sibling notebook tools.

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?

Description explains when to use each action and specifies required arguments per action (e.g., 'notebook_id required for action=run'). Does not explicitly mention when not to use or alternative tools, but context is sufficient for basic selection.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jacob-bd/notebooklm-mcp-cli'

If you have feedback or need assistance with the MCP directory API, please join our Discord server