observability_log_pattern_miner
As a CTO, extract anomalous log patterns from public breach reports (e.g., Verizon DBIR) and MITRE ATT&CK techniques to optimize SIEM rules and observability pipelines. Inputs include threat actor groups, MITRE tactics (e.g., 'TA0005'), or log sources (e.g., 'AWS CloudTrail'). Outputs structured ...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/observability-log-pattern-miner.md
What observability_log_pattern_miner does on Mcp Knowledge
AI agents call observability_log_pattern_miner 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 |
tactic | string | Yes | MITRE ATT&CK tactic ID (e.g., 'TA0005') |
technique | string | — | MITRE ATT&CK technique ID (e.g., 'T1059') |
log_source | string | — | Log source type (e.g., 'AWS CloudTrail', 'Windows Event Log') |
max_results | integer | — | |
threat_actor | string | — | Threat actor group name (e.g., 'APT29') |
Parameters from the server's own tool schema.
Why observability_log_pattern_miner is rated Low
This tool reads and analyzes publicly available threat intelligence data (Verizon DBIR, MITRE ATT&CK) to generate detection recommendations. It does not write, execute, or delete anything — it extracts and structures information from public sources. The output is advisory/analytical. Severity is low because it only reads from public datasets and produces recommendations, posing minimal blast radius if misused.
From the tool's definition extract anomalous log patterns from public breach reports (e.g., Verizon DBIR) and MITRE ATT&CK techniques... Outputs structured patterns with MITRE mappings, prevalence scores, and detection recommendations
Attacks that exploit this kind of access
The rule that runs observability_log_pattern_miner 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 observability_log_pattern_miner, this is the rule to start with:
observability_log_pattern_miner 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 observability_log_pattern_miner call is checked against it from then on.
Questions about observability_log_pattern_miner
As a CTO, extract anomalous log patterns from public breach reports (e.g., Verizon DBIR) and MITRE ATT&CK techniques to optimize SIEM rules and observability pipelines. Inputs include threat actor groups, MITRE tactics (e.g., 'TA0005'), or log sources (e.g., 'AWS CloudTrail'). Outputs structured patterns with MITRE mappings, prevalence scores, and detection recommendations. Ideal for reducing false positives and improving breach detection coverage. 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.
observability_log_pattern_miner accepts 6 parameters: async, tactic, technique, log_source, max_results, threat_actor. Required: tactic. 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 observability_log_pattern_miner: 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.
observability_log_pattern_miner 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 observability_log_pattern_miner 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 observability_log_pattern_miner. 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.
observability_log_pattern_miner 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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