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Glama

describe_topic

Inspect up to 20 Kafka topics in one call, returning partitions, offset ranges, message count estimates, timestamps, effective configuration inheritance, and per-topic errors.

Instructions

Describe 1 to 20 topics in one call through items: partitions, offset ranges, approximate message count, oldest/newest timestamps and complete effective configuration. Config entries identify whether values are inherited or topic-specific.

Results follow items order, each carrying index with result or error, so a topic that does not exist is reported against its own item rather than failing the call. Describing one topic is an items array of length one.

Message counts are offset spans and may overcount after retention or compaction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesThe topics to describe, 1 to 20 of them. Describing one topic is an array of length one. Results follow this order and a missing topic is reported against its own item.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
atomicYes
failedYes
appliedYes
resultsYes
succeededYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does so well: it discloses result ordering, per-item error isolation instead of call-level failure, config inheritance vs topic-specific values, and the important caveat that message counts can overcount after retention or compaction. It stops short of stating read-only semantics or performance/authorization behavior, so not a 5.

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?

Front-loaded with the action and its scope, then layered with result shape, error behavior, and the counting caveat. Every sentence carries distinct information and none is redundant filler.

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?

An output schema exists, so return values are already structured; the description usefully supplements it with ordering, per-item error semantics, and the overcount caveat. For a read-only batch-describe tool with one well-documented parameter, nothing needed for correct invocation is missing.

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

Parameters3/5

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

Schema description coverage is 100% and the single parameter is thoroughly documented there, so the baseline is 3. The description adds the 1-to-20 bound and ordering semantics, but those are already present in the schema's items description, so no meaningful value is added beyond the structured field.

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 and resource ('Describe 1 to 20 topics') and then enumerates exactly what the description returns: partitions, offset ranges, message counts, timestamps, and effective configuration. This is clearly distinguishable from list_topics (enumeration) and describe_consumer_group (different resource), even without an explicit sibling callout.

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?

It explains how to describe a single topic (items array of length one) and how per-item errors are reported, which is genuine usage context. However, it never states when to prefer this over list_topics or when describing is unnecessary, and gives no prerequisites. Usage is implied rather than directed.

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