feedback_save

SERVERDialogbrain SOURCEhttps://api.dialogbrain.com/mcp

Medium RISK CLASS

Category Write

Parameters 73 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-saloprj-dialogbrain/feedback-save.md

WHAT IT DOES

What feedback_save does on Dialogbrain

AI agents use feedback_save to create or update resources in Dialogbrain, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Dialogbrain environment.

Parameter	Type	Required	Description
`key`	string	Yes	Short identifier for this rule (e.g. 'reply_language', 'meeting_hours'). Must not start with '__' (reserved).
`why`	string	—	Why this rule matters (optional but recommended for the distiller).
`rule`	string	Yes	The rule itself, in imperative form. Required.
`scope`	string	Yes	Scope of the rule. 'workspace' for org-wide rules; 'agent' for per-agent overrides; 'thread' for conversation-specific guidance; 'person' for per-contact prefer
`how_to_apply`	string	—	When/how to apply the rule (optional). Helpful for conditional rules like 'apply when speaking to Russian-speaking customers'.
`scope_ref_id`	string	—	Required for scope='thread' (thread_id) and scope='person' (person_id).
`target_agent_id`	integer	—	Target agent. In agent mode optional (defaults to self); required from MCP. Ignored when scope='workspace'.

Parameters from the server's own tool schema.

RISK

Why feedback_save is rated Medium

The tool writes new configuration data (behavioral rules and preferences) into the system. While it doesn't execute external operations or delete data, it does persistently modify the agent's decision-making guidance.

From the tool's definition Tool creates and stores behavioral rules and preferences ('Save a behavioral rule, preference, or correction'). It modifies system state by recording guidance that 'should guide future agent behavior' across different scopes ('workspace', 'agent', 'person').

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs feedback_save safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Dialogbrain, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For feedback_save, this is the rule to start with:

feedback_save Rate-limited

feedback_save stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "feedback_save": {
      "limits": [
        {
          "counter": "feedback_save_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Dialogbrain, apply this rule, and every feedback_save call is checked against it from then on.

FAQ

Questions about feedback_save

What does the feedback_save tool do? +

Save a behavioral rule, preference, or correction that should guide future agent behavior. Use this when the user gives explicit guidance like 'always reply in Russian', 'don't suggest meetings before 11am', or 'invoice link goes via email, not chat'. Structure the rule as: the rule itself, why it matters (if stated), and how to apply it. Scope: 'workspace' for org-wide rules, 'agent' for per-agent overrides, 'person' for per-contact preferences. Prefer feedback.save over notes.save for anything that's instructive rather than informational. It is categorised as a Write tool in the Dialogbrain MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

What parameters does feedback_save accept? +

feedback_save accepts 7 parameters: key, why, rule, scope, how_to_apply, scope_ref_id, target_agent_id. Required: key, rule, scope. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on feedback_save? +

Register the Dialogbrain MCP server in PolicyLayer and add a rule for feedback_save: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Dialogbrain. Nothing to install.

What risk level is feedback_save? +

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

Can I rate-limit feedback_save? +

Yes. Add a rate_limit block to the feedback_save rule in your PolicyLayer 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 feedback_save completely? +

Set action: deny in the PolicyLayer policy for feedback_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 feedback_save? +

feedback_save is provided by the Dialogbrain MCP server (https://api.dialogbrain.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

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