Medium Risk

contentrain_content_save

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 ignor...

Part of the Contentrain MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.

@contentrain/mcp Write Risk 2/5

AI agents use contentrain_content_save to create or modify resources in Contentrain. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call contentrain_content_save repeatedly, creating or modifying resources faster than any human could review. Intercept's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Contentrain.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

io-github-contentrain-contentrain.yaml
tools:
  contentrain_content_save:
    rules:
      - action: allow
        rate_limit:
          max: 30
          window: 60

See the full Contentrain policy for all 15 tools.

Tool Name contentrain_content_save
Category Write
Risk Level Medium

View all 15 tools →

Agents calling write-class tools like contentrain_content_save have been implicated in these attack patterns. Read the full case and prevention policy for each:

Browse the full MCP Attack Database →

Other tools in the Write risk category across the catalogue. The same policy patterns (rate-limit, validate) apply to each.

What does the contentrain_content_save tool do? +

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". Changes are auto-committed to git — do NOT manually edit .contentrain/ files after calling this tool.. It is categorised as a Write tool in the Contentrain MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on contentrain_content_save? +

Add a rule in your Intercept YAML policy under the tools section for contentrain_content_save. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Contentrain MCP server.

What risk level is contentrain_content_save? +

contentrain_content_save is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit contentrain_content_save? +

Yes. Add a rate_limit block to the contentrain_content_save rule in your Intercept policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block contentrain_content_save completely? +

Set action: deny in the Intercept policy for contentrain_content_save. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides contentrain_content_save? +

contentrain_content_save is provided by the Contentrain MCP server (@contentrain/mcp). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policies on Contentrain

Open source. One binary. Zero dependencies.

npx -y @policylayer/intercept
github.com/policylayer/intercept →
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