sre_slo_breach_predictor
As a CTO, predict potential SLO breaches 24 hours in advance by analyzing public incident reports and MITRE ATT&CK techniques. Input your service's critical components and reliability thresholds to receive breach probability scores, top contributing TTPs, and recommended mitigations. Uses MITRE A...
This record as markdown: /tools/io-github-getgapup-gapup-mcp/sre-slo-breach-predictor.md
What sre_slo_breach_predictor does on Gapup Mcp
AI agents call sre_slo_breach_predictor to retrieve information from Gapup Mcp 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 |
|---|---|---|---|
async | boolean | — | If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client ti |
time_window_hours | number | — | |
service_components | array | Yes |
Parameters from the server's own tool schema.
Why sre_slo_breach_predictor is rated Low
The tool performs analysis and prediction by querying external public data sources (MITRE ATT&CK, GitHub Advisories, Cloudflare Radar) and returning scores and recommendations. It does not modify, execute, delete, or move money — it is a read/query operation producing analytical output.
From the tool's definition predict potential SLO breaches 24 hours in advance by analyzing public incident reports and MITRE ATT&CK techniques... receive breach probability scores, top contributing TTPs, and recommended mitigations
Attacks that exploit this kind of access
The rule that runs sre_slo_breach_predictor safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Gapup Mcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For sre_slo_breach_predictor, this is the rule to start with:
sre_slo_breach_predictor 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 Gapup Mcp, apply this rule, and every sre_slo_breach_predictor call is checked against it from then on.
Questions about sre_slo_breach_predictor
As a CTO, predict potential SLO breaches 24 hours in advance by analyzing public incident reports and MITRE ATT&CK techniques. Input your service's critical components and reliability thresholds to receive breach probability scores, top contributing TTPs, and recommended mitigations. Uses MITRE ATT&CK, GitHub Advisories, and Cloudflare Radar data. Pass async:true to avoid timeout. It is categorised as a Read tool in the Gapup Mcp MCP Server, which means it retrieves data without modifying state.
sre_slo_breach_predictor accepts 3 parameters: async, time_window_hours, service_components. Required: service_components. The full parameter table on this page comes from the server's own tool schema.
Register the Gapup MCP server in PolicyLayer and add a rule for sre_slo_breach_predictor: 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 Gapup Mcp. Nothing to install.
sre_slo_breach_predictor 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 sre_slo_breach_predictor 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 sre_slo_breach_predictor. 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.
sre_slo_breach_predictor is provided by the Gapup MCP server (https://mcp.gapup.io/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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