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list_systemic_patterns

Read-only

List the SYSTEMIC PATTERNS the platform tracks: issues that have recurred AND been remediated repeatedly, grouped by whoever can fix them at source. ALWAYS call this before diagnosing a recurring problem, and whenever the user asks why something keeps happening, keeps coming back, or has happened before. Each entry carries occurrence / distinct-cluster / distinct-day counts, how many times an agent remediated it, and an interpretation field stating what the row means for your answer - read that field before replying. A pattern with status 'escalated' has ALREADY been reported for a permanent fix (to the Ankra platform team as a support ticket, or to a stack profile's owner as a change request): say so and cite escalation_reference instead of re-diagnosing, and never report it again. Scope 'platform' means the defect arrives with every cluster Ankra provisions; 'stack_profile' means one profile version ships it to every deployment of that profile; 'local' is confined to one cluster and is never reported upstream. Read-only.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum patterns to return (default 10, max 50).
scopeNoOptional scope filter.
statusNoOptional lifecycle filter; 'escalated' lists what has already been reported for a permanent fix.
cluster_idYesAnkra cluster id (UUID) the call targets. Discover ids with list_clusters.
this_cluster_onlyNoRestrict to patterns concerning the selected cluster - its own local pattern plus the platform and stack-profile patterns it contributed occurrences to. Ignored when no cluster is selected.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only supply readOnlyHint=true, and the description adds substantial behavior beyond that: escalation already filed as a support ticket or change request, the semantics of each scope value, and the presence of an interpretation field that must be read before replying. That is exactly the kind of context structured fields cannot carry.

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?

Front-loads the core purpose, then usage triggers, then escalation behavior and scope semantics. Dense and mostly waste-free, though the scope-explanation sentence is long and could be tightened without losing meaning.

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?

No output schema exists, yet the description discloses the shape of each entry (occurrence/distinct-cluster/distinct-day counts, agent remediation count, interpretation field, escalation_reference). An agent has everything needed to call it and to act on the results correctly.

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 coverage is 100%, so baseline is 3, but the description meaningfully enriches two params: it defines what platform/stack_profile/local mean in terms of defect propagation, and clarifies that 'escalated' status lists already-reported items. It adds little for limit or this_cluster_only, which the schema already covers.

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?

States a specific verb (list) and a precisely defined resource: recurring issues that were remediated repeatedly, grouped by who can fix them at source. The scope taxonomy (platform vs stack_profile vs local) distinguishes it from diagnostic siblings like get_cluster_insights or list_security_findings.

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

Usage Guidelines5/5

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

Explicitly instructs 'ALWAYS call this before diagnosing a recurring problem' and enumerates trigger phrasings ('why something keeps happening', 'has happened before'). It also states the when-not: for 'escalated' patterns, cite escalation_reference instead of re-diagnosing and never report again.

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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