mcp-server-rabbitmq

Overview Schema Source Related Servers Discussions Admin

rabbitmq_list_channels

List active RabbitMQ channel metrics, including unacknowledged backlogs, prefetch, and consumer activity, to diagnose blocked consumption and abnormal client channels.

Instructions

查询活跃信道指标、未确认消息堆积、Prefetch 与消费活跃度.

用于精准排查客户端信道未确认消息积压、消费阻塞与信道异常。未配置 Management API 时依据 ADR-0001 优雅降级。

@param connection 目标 Broker 连接配置别名，默认为 'default' @param vhost 可选的虚拟主机名称过滤 @param connection_name 可选的客户端连接标识过滤 @return 结构化的信道诊断清单

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "rabbitmq_list_channels"
  }
}

Input Schema

Name	Required	Default
`vhost`	No
`connection`	No	default
`connection_name`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.4Oct 7, 2026

TDQS

A3.8/5.0

Behavior3/5

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

No annotations, so the description carries the full burden. It usefully discloses a behavioral trait — graceful degradation when the Management API is unconfigured (ADR-0001) — which the schema cannot convey. However, it omits auth/permission requirements, pagination, and any rate-limit behavior, leaving real gaps for a read tool with zero annotation coverage.

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?

Well front-loaded: purpose, then diagnostic use, then degradation note, then @param/@return tags. The structure is clean and each block earns its place, though the @param tags restate schema-level facts without much added depth.

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?

An output schema exists, so return detail needn't be explained, and the description focuses correctly on purpose, use case, degradation behavior, and parameters. It is nearly complete for a read-only channel listing tool; only auth and filtering-interaction details are missing.

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 description coverage is 0%, so the description must compensate, and it does: connection (alias, defaults to 'default'), vhost (optional filter), and connection_name (optional filter) are all documented with meaning. It does not clarify format/syntax of the alias or how the filters combine, keeping it from a 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (查询活跃信道指标 – query active channel metrics) and enumerates the concrete data surfaced: unacked message backlog, prefetch, and consumer activity. This clearly separates it from siblings like list_connections/list_queues via the 'channel' resource, but it never names the alternatives, so it stops short of a 5.

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?

Explicitly frames the use case: 用于精准排查客户端信道未确认消息积压、消费阻塞与信道异常 (for diagnosing unacked backlogs, consumption blocking, channel anomalies). That is clear when-to-use context, but there are no stated when-not conditions or comparisons to sibling tools.

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