Process a code change through the Change-Driven pipeline (Flow ④). Full pipeline: Diff → ChangeSet → Review → Blast Radius → Vault Classifies intent (bugfix/feature/refactor/test/security/performance), runs code review (hardcoded secrets, TODOs, broad exceptions, unsafe), computes belief impact, ...
AI agents invoke process_change to trigger actions in Entroly Context Engine. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool executes a multi-stage pipeline over code diffs, including automated code review, intent classification, blast radius computation, and persisting results to a Vault. It triggers a chain of external operations whose effects depend on the provided diff and metadata.
From the tool's definition 'Process a code change through the Change-Driven pipeline', 'Diff → ChangeSet → Review → Blast Radius → Vault', 'computes belief impact', 'runs code review (hardcoded secrets, TODOs, broad exceptions, unsafe)'
Documented attack patterns abuse exactly the kind of access process_change gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Entroly Context Engine, and nothing reaches the server without passing your rules. This is the rule we recommend for process_change:
{
"version": "1",
"default": "deny",
"tools": {
"process_change": {
"limits": [
{
"counter": "process_change_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}process_change stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Process a code change through the Change-Driven pipeline (Flow ④). Full pipeline: Diff → ChangeSet → Review → Blast Radius → Vault Classifies intent (bugfix/feature/refactor/test/security/performance), runs code review (hardcoded secrets, TODOs, broad exceptions, unsafe), computes belief impact, and returns a structured PR brief. Args: diff_text: Raw unified diff text (git diff output) commit_message: Optional commit message for intent classification pr_title: Optional PR title. It is categorised as a Execute tool in the Entroly Context Engine MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Entroly Context Engine MCP server in PolicyLayer and add a rule for process_change: 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 Entroly Context Engine. Nothing to install.
process_change is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the process_change 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 process_change. 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.
process_change is provided by the Entroly Context Engine MCP server (juyterman1000/entroly). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Entroly Context Engine, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
52 Entroly Context Engine tools catalogued and risk-classified — across an index of 43,000+ MCP servers.