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observability_log_pattern_miner

Read-onlyIdempotent

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf 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 timeouts.
tacticYesMITRE ATT&CK tactic ID (e.g., 'TA0005')
techniqueNoMITRE ATT&CK technique ID (e.g., 'T1059')
log_sourceNoLog source type (e.g., 'AWS CloudTrail', 'Windows Event Log')
max_resultsNo
threat_actorNoThreat actor group name (e.g., 'APT29')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesYes
metadataNo
patternsYes
warningsYes

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, which are not contradicted. The description adds the async behavior detail (timeout avoidance) but does not fully disclose other behavioural traits like data freshness or potential delays. With annotations covering the safety profile, a score of 3 is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is reasonably concise at four sentences, though the opening 'As a CTO' is slightly superfluous. The key information is front-loaded, and each sentence contributes to understanding the tool's purpose and usage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has a rich output schema, the description covers inputs, outputs, async usage, and purpose comprehensively. It provides sufficient context for an agent to select and invoke the tool correctly, with no major gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all 6 parameters with descriptions, but the description adds value by explaining the broader context of outputs (structured patterns with MITRE mappings) and the purpose of inputs (e.g., 'threat actor groups'). This goes beyond the schema's parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool extracts anomalous log patterns from public breach reports and MITRE ATT&CK techniques to optimize SIEM rules. It distinguishes itself from sibling tools like 'observability_metric_anomaly_detector' by focusing on log patterns from breach reports rather than metric anomalies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context for use ('Ideal for reducing false positives and improving breach detection coverage') and mentions async parameter to avoid timeout. However, it does not explicitly state when to use this tool over alternatives or when not to use it, leaving room for ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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