notes_recall
Recall notes from your notebook. By default returns only your own notes (all scopes, newest first). Pass filter_agent_id=<int> to read another agent's notebook, or filter_agent_id="all" (or "*") to read across every agent in the workspace. Pass scope to narrow to global/thread/person. Each result...
This record as markdown: /tools/io-github-saloprj-dialogbrain/notes-recall.md
What notes_recall does on Dialogbrain
AI agents call notes_recall to retrieve information from Dialogbrain without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
key | string | — | Recall a specific note by key |
limit | integer | — | Max notes (default 20, max 50). Newest first. |
scope | string | — | Optional filter: global | thread | person. Omit for all scopes. |
scope_ref_id | string | — | Filter by specific thread_id or person_id |
filter_agent_id | string | — | Optional. Omit to read only your own notes. Pass a numeric agent_id as a string (e.g. "57") to read another agent's notebook (read-only). Pass "all" or "*" to r |
Parameters from the server's own tool schema.
Why notes_recall is rated Low
This is fundamentally a Read operation: it retrieves/queries notebook data without side effects. The severity is medium rather than low because the tool can access sensitive notes across all agents in a workspace when filter_agent_id='all' is used, creating potential for information leakage if an AI agent is compromised or misused.
From the tool's definition Tool description uses 'Recall notes' and 'read another agent's notebook' and 'read across every agent' — purely retrieval operations with no modification.
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs notes_recall 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 notes_recall, this is the rule to start with:
notes_recall is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Dialogbrain, apply this rule, and every notes_recall call is checked against it from then on.
Questions about notes_recall
Recall notes from your notebook. By default returns only your own notes (all scopes, newest first). Pass filter_agent_id=<int> to read another agent's notebook, or filter_agent_id="all" (or "*") to read across every agent in the workspace. Pass scope to narrow to global/thread/person. Each result includes agent_id and agent_name of the author. It is categorised as a Read tool in the Dialogbrain MCP Server, which means it retrieves data without modifying state.
notes_recall accepts 5 parameters: key, limit, scope, scope_ref_id, filter_agent_id. The full parameter table on this page comes from the server's own tool schema.
Register the Dialogbrain MCP server in PolicyLayer and add a rule for notes_recall: 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.
notes_recall is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the notes_recall 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.
Set action: deny in the PolicyLayer policy for notes_recall. 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.
notes_recall 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.
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