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BhaumikAbhishek

OCI Kafka MCP Server

oci_kafka_get_partition_skew

Detect partition skew in Kafka clusters by comparing leader distribution across brokers. Identify imbalances above a 1.5 skew ratio that degrade performance.

Instructions

Detect partition imbalance across brokers.

Checks if partitions are evenly distributed across brokers (as leaders). A skew ratio > 1.5 indicates significant imbalance that may cause performance degradation.

Args: topic_name: Optional topic to check. If not provided, checks all topics.

Returns skew ratio, per-broker partition counts, and a recommendation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topic_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Describes that it checks partition leadership distribution, defines skew ratio threshold, and states return values. Since no annotations, description provides adequate behavioral context. Does not mention permissions but read-only nature inferred.

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

Conciseness5/5

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

Concise and well-structured. Front-loaded purpose, includes threshold interpretation, then parameter, then returns. No superfluous information.

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 simple tool with one optional parameter and output schema, description covers all necessary details: what it does, how it interprets results, what it returns. Sufficient for agent decision-making.

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

Parameters5/5

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

Only one parameter fully explained: topic_name is optional, default checks all topics. This adds complete meaning beyond schema which has no 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?

Clearly states tool detects partition imbalance across brokers. Specific verb+resource, and distinguishes from sibling tools like detect_under_replicated_partitions. Provides threshold for interpretation.

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?

No explicit when-to-use or alternatives mentioned. Implied from purpose but lacks comparison to similar detection tools. Could be improved by noting when to use this versus other health checks.

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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