claim_next_work_item_qa_pass
Atomically choose and take the first workable QA pass for ONE exact product in ONE requested gate across every board this worker can see. Product is read from the card, never inferred from the board that happens to hold it. QA1 and QA2 pools must name their gate explicitly; a QA1 drain can never ...
This record as markdown: /tools/adrata-adrata-mcp/claim-next-work-item-qa-pass.md
What claim_next_work_item_qa_pass does on Adrata
AI agents call claim_next_work_item_qa_pass to retrieve information from Adrata without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why claim_next_work_item_qa_pass is rated Low
Even though claim_next_work_item_qa_pass only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs claim_next_work_item_qa_pass 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 claim_next_work_item_qa_pass, this is the rule to start with:
claim_next_work_item_qa_pass is read-only, so it stays allowed. 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 claim_next_work_item_qa_pass call is checked against it from then on.
Questions about claim_next_work_item_qa_pass
Atomically choose and take the first workable QA pass for ONE exact product in ONE requested gate across every board this worker can see. Product is read from the card, never inferred from the board that happens to hold it. QA1 and QA2 pools must name their gate explicitly; a QA1 drain can never consume QA2 work and vice versa. Concurrent sessions receive distinct cards; one MCP worker holds at most one pass at a time. An empty result is TWO different situations and the response tells them apart: an empty skippedBlocked means no eligible pass remains in that product/gate, so move to another lane; a non-empty skippedBlocked means there IS work here and every card is waiting on another card, so read the blockers rather than re-claiming. The response returns the selected board and card, explains the selection, and carries the exact acceptance criteria and recorded-QA requirements. Capability material stays inside this MCP process.${GOVERNED_NOTE}. It is categorised as a Read tool in the Adrata MCP Server, which means it retrieves data without modifying state.
Register the Adrata MCP server in PolicyLayer and add a rule for claim_next_work_item_qa_pass: 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.
claim_next_work_item_qa_pass 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 claim_next_work_item_qa_pass 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 claim_next_work_item_qa_pass. 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.
claim_next_work_item_qa_pass 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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