Skip to main content
Glama

contentrain_content_save

Idempotent

Save content entries in Dictionary, Collection, Document, or Singleton format, with support for media fields, locales, and automatic git commits.

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
Behavior4/5

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

Annotations already provide readOnlyHint=false, destructiveHint=false, idempotentHint=true. The description adds valuable behavioral traits: auto-commit to git and media field normalization behavior, which go beyond annotations without contradiction.

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 relatively long but well-structured with clear sections for each model kind. It is front-loaded with the main purpose and every sentence adds value, though could be slightly more concise.

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 save operation with no output schema, the description covers input variations comprehensively and includes important context like git auto-commit and media field handling. It does not describe errors or return values, which is acceptable given the tool type.

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 coverage is 100% with descriptions for all properties. The description adds significant meaning by explaining how 'id', 'slug', and 'data' are treated per model kind, and specifics about media fields, enhancing understanding beyond the schema.

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 clearly states 'Save content entries' and provides detailed explanations for each model kind (DICTIONARY, COLLECTION, DOCUMENT, SINGLETON), which is specific and distinguishes this tool from siblings like contentrain_content_delete and 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 implicitly indicates when to use this tool (to save content entries) and includes a warning against manually editing .contentrain/ files after calling, but does not explicitly mention alternatives or when not to use it. Given no sibling save tools, this is adequate.

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/Contentrain/ai'

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