Kafka MCP

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delete_records

Truncate Kafka partitions by deleting records below a given offset while keeping the topic, its config, and consumer groups; preview data loss before confirming.

Instructions

Delete the oldest messages of 1 to 100 partitions in one call through items, without deleting the topic. Each item removes every message of one partition with an offset lower than before_offset; the message at before_offset becomes the first readable one. Use it to purge test data, or a run of bad messages at the head of a partition, while keeping the topic, its configuration and its consumer groups. Truncating one partition is an items array of length one.

No message is deleted unless confirm is true and the item sets acknowledge_data_loss. The preview reports the partition's start and end offsets, how many messages would be removed, and every consumer group whose committed offset is below the cut, with how many messages it would lose without ever processing them. Such a group resumes from the new start offset.

Kafka cannot restore deleted records. One confirm covers the whole batch, and deletion is not atomic: partitions truncated before a later item failed stay truncated. Results follow items order, each carrying index with result or error. Compacted topics are not supported by every broker. Requires Kafka DELETE permission on the topic.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`items`	Yes	The partitions to truncate, 1 to 100 of them. Truncating one partition is an array of length one. Two items naming the same topic and partition are refused before anything is deleted.
`confirm`	No	Optional. When false or omitted, nothing is deleted and the response shows how many messages each item would remove and which consumer groups have not read them yet. Must be true to delete. One confirm covers the whole batch.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`atomic`	Yes
`failed`	Yes
`applied`	Yes
`results`	Yes
`succeeded`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0Oct 7, 2026

TDQS

A4.4/5.0

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 thoroughly: the two-key safety gate (confirm plus per-item acknowledge_data_loss), the dry-run preview contents, irreversibility, the fact that one confirm covers the whole batch, non-atomic partial failure leaving earlier partitions truncated, and the Kafka DELETE permission requirement. These are exactly the traits an agent cannot infer from the schema.

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-loaded with the core action and semantics, and every paragraph carries real information. Minor redundancy remains, such as the length-one array note appearing in both description and schema.

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?

For a destructive batch operation with an output schema present, the description covers the safety model, failure mode, permissions, and per-item result shape without needing to explain return values. An agent has everything required to invoke it correctly.

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%, so items, topic, partition, before_offset, acknowledge_data_loss and confirm are already fully documented in the schema. The description restates much of this (batch-wide confirm, length-one array) rather than adding new syntax or constraints, so the baseline 3 applies.

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 with precise scope: 'Delete the oldest messages of 1 to 100 partitions in one call ... without deleting the topic.' It immediately distinguishes itself from the sibling delete_topic by clarifying the topic itself survives.

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?

Gives concrete use cases ('purge test data, or a run of bad messages at the head of a partition') and an implicit when-not ('while keeping the topic, its configuration and its consumer groups'). It never names delete_topic directly as the alternative, so routing relies on inference rather than an explicit pointer.

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