Skip to main content
Glama

delete_consumer_group

Delete Kafka consumer groups and their committed offsets to clean up abandoned groups that still report lag.

Instructions

Delete 1 to 100 consumer groups in one call through items, removing each group's committed offsets. Use it to clean up groups whose consumers were decommissioned: their commits keep reporting lag that nobody will ever drain. Deleting one group is an items array of length one.

No group is deleted unless confirm is true. The preview lists each group's state, the topics it has committed on, every committed offset with its lag, and the total lag that would disappear with it. A group with active members is refused: it is not abandoned, and deleting it would reset where its consumers resume.

If a consumer later starts with the same group id, it begins wherever its auto.offset.reset points, not where the group left off. One confirm covers the whole batch, and deletion is not atomic: groups deleted before a later item failed stay deleted. Results follow items order, each carrying index with result or error. Requires Kafka DELETE permission on the group.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesThe groups to delete, 1 to 100 of them. Deleting one group is an array of length one. Naming the same group twice is refused before anything is deleted.
confirmNoOptional. When false or omitted, nothing is deleted and the response shows each group's committed offsets and lag. Must be true to delete. One confirm covers the whole batch.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
atomicYes
failedYes
appliedYes
resultsYes
succeededYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so: confirm gating, refusal on active members, non-atomic batch semantics ('groups deleted before a later item failed stay deleted'), per-item result/error ordering, required Kafka DELETE permission, and the offset-reset consequence for a re-created group id.

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 scope, then proceeds through preview, refusal, and failure semantics. Length is justified by the destructive, unannotated nature of the tool, and no sentence is 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?

Output schema exists so return values needn't be fully described, yet the description still covers result ordering and error shape. Safety, permissions, batch limits, and idempotency-adjacent caveats are all present for a high-risk mutation.

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 the baseline is 3, but the description adds real meaning beyond the schema: 'Deleting one group is an items array of length one,' 'One confirm covers the whole batch,' and the ordered per-item index/result/error shape.

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 ('Delete 1 to 100 consumer groups'), names the batch mechanism (items), and specifies the side effect ('removing each group's committed offsets'). This clearly separates it from siblings like delete_topic or describe_consumer_group.

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?

Gives the explicit use case (groups whose consumers were decommissioned, still reporting undrainable lag), the refusal condition (active members), and the safe-preview alternative via confirm=false. An agent knows both when to call it and when not to.

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