Skip to main content
Glama

get_consumer_group_offsets

Fetch committed offsets, high/low watermarks, and lag for each partition of a specified consumer group and topic.

Instructions

Get the committed offsets and lag for a specific consumer group and topic. Returns the committed offset, high/low watermarks, and calculated lag for each partition.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
group_idYes
topic_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It accurately describes the output (committed offset, watermarks, lag) but does not mention read-only nature, authentication needs, rate limits, or error conditions. It adds moderate value beyond the bare minimum.

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?

The description is extremely concise: two sentences, front-loaded with the purpose, followed by details of return values. No unnecessary words, making it efficient for an AI agent to parse.

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 tool's simplicity (2 required params, output schema exists), the description covers the core purpose and expected return. However, it omits potential error conditions (e.g., group not found) and usage context, which would be beneficial for a Kafka tool. Overall, it is nearly complete for a read operation.

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

Parameters2/5

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

The input schema has 0% description coverage, so the description must clarify parameter meaning. It implicitly links group_id and topic_name to 'specific consumer group and topic', but provides no further detail on format, constraints, or examples. This is only minimal added value.

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?

The description clearly states the verb 'get', the resource 'committed offsets and lag', and specifies it is for a specific consumer group and topic. This effectively distinguishes it from sibling tools like describe_consumer_group or reset_consumer_group_offset, which have different purposes.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus its siblings. It does not mention prerequisites, such as requiring an existing consumer group or topic, nor does it exclude scenarios where other tools might be more appropriate.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/wklee610/kafka-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server