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 call qc_learnings to retrieve information from UnClick without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| 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 Low
Read-only lookup of a hard-coded knowledge library with no side effects.
From the tool's definition Query... No arguments returns the index; filter with id, tag, or query
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 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 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 Read tool in the UnClick MCP Server, which means it retrieves data without modifying state.
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 Read tool with low risk. Read-only tools are generally safe to allow by default.
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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