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Aguantar

kafka-dataops-mcp

by Aguantar

kafka_topic_info

Inspect Kafka topics to get partition details, replication health, and retention warnings. Diagnose writes failing or degraded conditions based on ISR versus min.insync.replicas.

Instructions

Get topic details: partitions, replicas, ISR, configs, and health diagnosis.

Checks replication health:

  • ISR < min.insync.replicas = writes FAILING (critical)

  • ISR < replication.factor but >= min.insync.replicas = degraded (warning)

  • Retention policy info (size-based deletion warnings)

Args: topic: Topic name to inspect

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Without annotations, the description carries the burden of behavioral disclosure. It states it is a read operation (Get topic details) and includes specific health diagnosis logic, but does not explicitly confirm non-destructiveness or any additional behavioral traits.

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?

Description is front-loaded with the core purpose, followed by organized health check details and parameter documentation. No wasted sentences, highly efficient.

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

Completeness4/5

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

Given the presence of an output schema (not shown but flagged), the description need not detail return values. It covers parameter, health diagnosis, and retention policy. Slight gap on error handling but adequate for this tool.

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?

Input schema has 0% description coverage, but the description's Args section clearly explains the 'topic' parameter as 'Topic name to inspect', compensating for the schema gap.

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?

Description clearly states it retrieves detailed topic information including partitions, replicas, ISR, configs, and health diagnosis, distinguishing it from sibling tools like kafka_broker_status (broker-level), kafka_consumer_lag (consumer lag), and kafka_list_topics (listing).

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

Usage Guidelines4/5

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

Description implicitly guides usage by detailing what health checks are performed, but does not explicitly mention when to use this tool over siblings or when not to use it.

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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