qc_learnings
The hard-coded QC learnings library: field-proven failure patterns (silent test passes, false-red CI, deploy verification traps, release gate ordering) with the exact recipes that fixed them. Query BEFORE debugging a red CI, a suspicious green suite, or a deploy mismatch. No arguments returns the...
This record as markdown: /tools/io-github-malamutemayhem-unclick-mcp-server/qc-learnings.md
What qc_learnings does on UnClick
AI agents invoke qc_learnings to trigger actions in UnClick. 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.
| Parameter | Type | Required | Description |
|---|---|---|---|
id | string | — | Exact learning id (e.g. pipe-eats-exit-code) for one full entry. |
tag | string | — | Filter by tag (e.g. ci, release, deploy, tests, git). |
query | string | — | Free-text search across symptoms, causes and recipes. |
Parameters from the server's own tool schema.
Why qc_learnings is rated High
qc_learnings 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 qc_learnings safely
PolicyLayer is an MCP gateway: it sits between your AI agents and UnClick, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For qc_learnings, this is the rule to start with:
qc_learnings 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 UnClick, apply this rule, and every qc_learnings call is checked against it from then on.
Questions about qc_learnings
The hard-coded QC learnings library: field-proven failure patterns (silent test passes, false-red CI, deploy verification traps, release gate ordering) with the exact recipes that fixed them. Query BEFORE debugging a red CI, a suspicious green suite, or a deploy mismatch. No arguments returns the index; filter with id, tag, or query. It is categorised as a Execute tool in the UnClick MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
qc_learnings accepts 3 parameters: id, tag, query. The full parameter table on this page comes from the server's own tool schema.
Register the UnClick MCP server in PolicyLayer and add a rule for qc_learnings: 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 UnClick. Nothing to install.
qc_learnings 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 qc_learnings 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 qc_learnings. 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.
qc_learnings is provided by the UnClick MCP server (@unclick/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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