Bagel

Save a pipeline to a YAML file

save_pipeline

Idempotent

Save a pipeline configuration to a YAML file for reuse or later execution. Returns the file path so you can edit or run it with run.py.

Instructions

Persist a pipeline configuration to a YAML file so it can be reused, edited, or run later with run.py. Returns the path to the written file.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes
`config`	Yes
`directory`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv2.3.1Sep 25, 2026
- addedInput schema / properties / directory / anyOf
```
Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
```
- changedInput schema / properties / directory / default
```
Previous value: -"pipelines"New value: +null
```
- removedInput schema / properties / directory / type
```
Removed value: -"string"
```
First observedv2.0.1Aug 13, 2026

TDQS

B3.3/5.0

Behavior3/5

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

The description discloses that the operation writes to a YAML file and returns the file path, which adds useful behavioral context. It does not mention overwrite behavior or file-naming conventions, but the annotations already cover idempotency and non-destructiveness.

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 front-loads the action and resource, includes the key return value, and contains no filler. It is appropriately sized for the tool's purpose.

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

Completeness2/5

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

For a tool with three parameters and zero schema descriptions, the description leaves too much unspecified—config shape, directory behavior, file extension, and overwrite semantics. The return path is stated, but an agent cannot reliably construct arguments from the description alone.

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

Parameters1/5

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

Schema description coverage is 0%, and the description provides no meaningful detail about the 'config' object structure, the 'name' parameter, or the optional 'directory'. The generic phrase 'pipeline configuration' is the only hint and is insufficient for correct invocation.

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 uses a specific verb ('Persist') and clearly identifies the resource ('pipeline configuration') and output format ('YAML file'). It is clear about what the tool does, though it does not explicitly differentiate from sibling tools like run_pipeline or preview_pipeline.

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?

The phrase 'so it can be reused, edited, or run later with run.py' provides clear context for when to use the tool. However, it does not explicitly state when not to use it or name alternative tools.

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