add_observations · DevFlow MCP: Risk & Policy

WHAT IT DOES

What add_observations does on DevFlow MCP

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

WHY IT NEEDS A POLICY

Why add_observations needs a policy

This tool creates new data records (observations) within the knowledge graph, which is a write operation. It is reversible via the sibling tool delete_observations. Severity is medium because misuse could pollute the knowledge graph with incorrect observations, degrading agent reasoning and decision-making, but the impact is limited to a single database and does not cross financial, destructive, or system-execution…

From the tool's definition Tool description states it 'Add[s] new observations to existing entities' and 'do not support...metadata', indicating the operation creates new records within the knowledge graph.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

add_observations Rate-limited

add_observations 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": {
    "add_observations": {
      "limits": [
        {
          "counter": "add_observations_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches DevFlow MCP, so you can run agents against it safely.

FAQ

Questions about add_observations

What does the add_observations tool do? +

Add new observations to existing entities in your DevFlow MCP knowledge graph memory. Observations are atomic facts about entities and do not support strength, confidence, or metadata (use relations for those features). It is categorised as a Write tool in the DevFlow MCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on add_observations? +

Register the DevFlow MCP server in PolicyLayer and add a rule for add_observations: 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 DevFlow MCP. Nothing to install.

What risk level is add_observations? +

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

Can I rate-limit add_observations? +

Yes. Add a rate_limit block to the add_observations 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 add_observations completely? +

Set action: deny in the PolicyLayer policy for add_observations. 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 add_observations? +

add_observations is provided by the DevFlow MCP server (takin-profit/devflow-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.