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contentrain_content_save

Idempotent

Save content entries across dictionary, collection, document, and singleton models. Handles media fields and auto-commits changes to git.

Instructions

Save content entries. Entry format varies by model kind: DICTIONARY — provide "locale" and "data" (flat key-value, all string values); "id" and "slug" are ignored; data keys are the identities. COLLECTION — provide "locale" and "data"; "id" is optional (auto-generated if omitted); "slug" is ignored. DOCUMENT — provide "slug" (required), "locale", and "data"; use the "body" key inside data for markdown content. SINGLETON — provide only "locale" and "data". MEDIA FIELDS (image/video/file): for a media-library asset, pass its storage path ("media/...") or URL; in cloud mode these are automatically normalized to absolute public delivery URLs on save (in markdown bodies too), so saved content renders in a browser anywhere with no SDK — in local mode the relative path is kept as-is. For external images (e.g. a CDN or Unsplash URL), pass the URL directly; it is saved untouched. Changes are auto-committed to git — do NOT manually edit .contentrain/ files after calling this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesModel ID
entriesYesContent entries to save
Behavior5/5

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

The description adds significant behavioral context beyond annotations: it discloses that changes are auto-committed to git, that media paths are normalized in cloud mode, and that fields like 'id' or 'slug' are ignored per model kind. No contradiction with annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false).

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 description is long but well-structured: purpose first, then model-specific rules, then media handling, then a warning. Every section adds value, though some sentences could be tightened (e.g., the media section is slightly verbose).

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?

The description covers all necessary aspects for a content save tool: multiple model kinds, media field behavior, auto-git-commit, and parameter interactions. No output schema exists, but the description explains the save outcome sufficiently.

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 100%, but the description adds crucial meaning: it explains which parameters are optional/ignored per model kind, clarifies the 'body' key for documents, and details media field handling. This goes far beyond the schema's basic descriptions.

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?

The description starts with 'Save content entries' and then elaborates with model-specific formats (DICTIONARY, COLLECTION, DOCUMENT, SINGLETON) and media field handling, making the tool's purpose clear and distinct from sibling tools like contentrain_content_delete or contentrain_content_list.

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 description provides explicit guidance on when to use each parameter per model kind, and warns against manual edits after calling the tool. It also explains media field behavior in cloud vs local mode, though it does not explicitly compare to alternatives (which are not applicable here).

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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