rq-mcp

summarize_failures

Read-onlyIdempotent

Group failed Redis Queue jobs by function and exception type to pinpoint recurring errors. Get counts, an example job ID, and failure timestamps for each group.

Instructions

Group a queue's failed jobs by function name + exception type.

Scans up to limit failed jobs (default 200) and returns one entry per (function, exception type) group: count, an example job ID, and first/last failure timestamps.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "queue_name": "<queue_name>"
    },
    "name": "summarize_failures"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`queue_name`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 25, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context beyond that by disclosing that it scans up to `limit` failed jobs (default 200) and aggregates results per group. This informs the agent about potential partial coverage and the exact return shape.

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 two sentences with no filler. The primary purpose is front-loaded in the first sentence, and the second sentence efficiently details the scanning behavior and output structure. Every word earns its place.

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 read-only summary tool with only two simple parameters and an available output schema, the description covers all essential operational details: grouping keys, limit behavior, and the fields in each group. There are no significant gaps that would prevent correct invocation.

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?

With schema description coverage at 0%, the description compensates by explaining `limit` as the scan cap and mentioning the default 200. It also clarifies that `queue_name` identifies the queue being summarized. Both parameters receive practical meaning that the raw schema lacks.

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 states a specific verb ('Group') and resource ('a queue's failed jobs') along with the grouping keys (function name + exception type) and the output fields. This clearly distinguishes it from sibling tools like list_jobs or get_job, which operate on individual jobs or queues.

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?

The description implies usage by explaining the aggregation behavior, but it does not explicitly state when to prefer this tool over alternatives such as list_jobs. No exclusions or when-not-to-use conditions are provided, so guidance is left to inference.

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

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