save_memory
Store a fact, insight, preference, or deal context. Memories persist across conversations so Claude remembers important information. Free tier stores locally; Pro+ stores server-side with vector search.
This record as markdown: /tools/adrata-starfield-mcp/save-memory.md
What save_memory does on Starfield
AI agents use save_memory to create or update resources in Starfield, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Starfield environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
tags | array | — | Optional tags for categorization (e.g. ["competitor", "payments"]) |
content | string | Yes | The fact or insight to remember (e.g. "Stripe is our top competitor in payments") |
memory_type | string | — | Type of memory (default: fact) |
Parameters from the server's own tool schema.
Why save_memory is rated Medium
An AI agent can call save_memory faster than any human can review: one bad instruction and it creates or modifies resources in Starfield by the hundred, each call as confident as the last.
Risk signalsAccepts raw HTML/template content (content)
Attacks that exploit this kind of access
The rule that runs save_memory safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Starfield, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For save_memory, this is the rule to start with:
save_memory 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Starfield, apply this rule, and every save_memory call is checked against it from then on.
Questions about save_memory
Store a fact, insight, preference, or deal context. Memories persist across conversations so Claude remembers important information. Free tier stores locally; Pro+ stores server-side with vector search. It is categorised as a Write tool in the Starfield MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
save_memory accepts 3 parameters: tags, content, memory_type. Required: content. The full parameter table on this page comes from the server's own tool schema.
Register the Starfield MCP server in PolicyLayer and add a rule for save_memory: 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 Starfield. Nothing to install.
save_memory is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the save_memory 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 save_memory. 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.
save_memory is provided by the Starfield MCP server (@adrata/starfield-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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