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-mcp-knowledge/sre-slo-breach-predictor.md
What sre_slo_breach_predictor does on Mcp Knowledge
AI agents call sre_slo_breach_predictor to retrieve information from Mcp Knowledge 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 reads and analyzes external public data sources to generate breach probability scores and mitigations. It does not modify, delete, or execute anything — it is purely analytical/read-oriented. Severity is medium because misuse could lead to bad reliability decisions, but the tool itself has no write or destructive capability.
From the tool's definition 'predict potential SLO breaches 24 hours in advance by analyzing public incident reports and MITRE ATT&CK techniques' — retrieves and analyzes public data sources (MITRE ATT&CK, GitHub Advisories, Cloudflare Radar) to produce predictions and recommendations
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 Mcp Knowledge, 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 Mcp Knowledge, 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 Mcp Knowledge 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 Mcp Knowledge 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 Mcp Knowledge. 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 Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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