record_work_item_local_proof
Record that ONE acceptance criterion was checked LOCALLY, at a named commit, with the command that was run and where its output lives. This is the gate in front of staging, and it is a different record from a QA receipt. A QA receipt says an independent credential re-ran a criterion against a dep...
This record as markdown: /tools/adrata-adrata-mcp/record-work-item-local-proof.md
What record_work_item_local_proof does on Adrata
AI agents invoke record_work_item_local_proof to trigger actions in Adrata. 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.
Why record_work_item_local_proof is rated High
record_work_item_local_proof triggers real processes with real consequences. An agent gone sideways doesn't fire it once. It starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Attacks that exploit this kind of access
The rule that runs record_work_item_local_proof safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Adrata, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For record_work_item_local_proof, this is the rule to start with:
record_work_item_local_proof 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Adrata, apply this rule, and every record_work_item_local_proof call is checked against it from then on.
Questions about record_work_item_local_proof
Record that ONE acceptance criterion was checked LOCALLY, at a named commit, with the command that was run and where its output lives. This is the gate in front of staging, and it is a different record from a QA receipt. A QA receipt says an independent credential re-ran a criterion against a deployment; this says the builder ran it on their own machine before asking for a deploy at all. It cannot be filed as QA evidence and it opens no QA gate. A card is. It is categorised as a Execute tool in the Adrata MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Adrata MCP server in PolicyLayer and add a rule for record_work_item_local_proof: 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 Adrata. Nothing to install.
record_work_item_local_proof 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 record_work_item_local_proof 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 record_work_item_local_proof. 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.
record_work_item_local_proof is provided by the Adrata MCP server (@adrata/adrata-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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