Save large tool output or observation to a file in .agent_memory
Accepts freeform code/query input (command); Accepts raw HTML/template content (content)
Part of the Context Engineering MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents use save_observation to create or modify resources in Context Engineering. 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 save_observation 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 Context Engineering.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
tools:
save_observation:
rules:
- action: allow
rate_limit:
max: 30
window: 60See the full Context Engineering policy for all 4 tools.
Agents calling write-class tools like save_observation have been implicated in these attack patterns. Read the full case and prevention policy for each:
Other tools in the Write risk category across the catalogue. The same policy patterns (rate-limit, validate) apply to each.
Save large tool output or observation to a file in .agent_memory. It is categorised as a Write tool in the Context Engineering MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Add a rule in your Intercept YAML policy under the tools section for save_observation. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Context Engineering MCP server.
save_observation 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_observation 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 save_observation. 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_observation is provided by the Context Engineering MCP server (context-engineering-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