Get the current user's cognitive identity and active session context. Call this at the START of a conversation to understand who you're talking to — their role, expertise, current project, and recent memory themes. This is the core of Purmemo's identity layer: once set in the dashboard, your id...
Bulk/mass operation — affects multiple targets
Part of the Pūrmemo MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents call get_user_context to retrieve information from Pūrmemo without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though get_user_context only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
tools:
get_user_context:
rules:
- action: allowSee the full Pūrmemo policy for all 11 tools.
Agents calling read-class tools like get_user_context have been implicated in these attack patterns. Read the full case and prevention policy for each:
Other tools in the Read risk category across the catalogue. The same policy patterns (rate-limit, allow) apply to each.
Get the current user's cognitive identity and active session context. Call this at the START of a conversation to understand who you're talking to — their role, expertise, current project, and recent memory themes. This is the core of Purmemo's identity layer: once set in the dashboard, your identity travels silently to every AI session so you're never explaining yourself from scratch again. WHAT IT RETURNS: - identity: role, expertise areas, primary domain, work style, preferred tools - current_session: what the user is working on right now (project, focus) - memory_summary: 2-3 sentence synthesis of the user's most recent memory themes WHEN TO CALL: - At the start of every new session (add to Claude system prompt) - When user says "load my context" or "what do you know about me?" - Before making recommendations that depend on knowing the user's background EXAMPLE USAGE: → User starts new Claude session → Claude calls get_user_context automatically → Response: { role: "founder", expertise: ["product", "fullstack"], project: "purmemo", focus: "identity layer", memory_summary: "Chris has been building Purmemo's..." } → Claude responds with full context already loaded — no re-explaining needed. It is categorised as a Read tool in the Pūrmemo MCP Server, which means it retrieves data without modifying state.
Add a rule in your Intercept YAML policy under the tools section for get_user_context. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Pūrmemo MCP server.
get_user_context 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 get_user_context 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.
Set action: deny in the Intercept policy for get_user_context. 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.
get_user_context is provided by the Pūrmemo MCP server (purmemo/purmemo-mcp). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Open source. One binary. Zero dependencies.
npx -y @policylayer/intercept