Skip to main content
Glama
ethanasm

mcp-queue-doctor

by ethanasm

Queue overview

queue_overview
Read-onlyIdempotent

Get a per-queue health snapshot with job state counts, stuck active jobs, completions, failures, and queue limits. Compare windowed versus all-time failures to spot retry storms or stuck jobs.

Instructions

Per-queue health snapshot: job counts by state, jobs stuck active, completions and failures in the window, and each queue's configured expiry, retention and retry limit. Failed counts are reported both within the window and all-time, because the two disagreeing is itself diagnostic.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowHoursNoHow far back to count completions and failures.
includeInternalNoInclude pg-boss's own internal queues.
stuckAfterMinutesNoActive duration past which a job counts as stuck.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.2

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds valuable behavioral nuance: it explains that failed counts are reported both within-window and all-time, and that a discrepancy between them is itself diagnostic. This goes beyond annotations and helps the agent interpret results.

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?

Two dense sentences with no waste. The core purpose is front-loaded, and the second sentence adds a useful diagnostic insight without bloating the description. Every word earns its place.

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?

For a read-only snapshot tool with no output schema, the description lists the key return elements (job counts by state, stuck active, completions, failures, and configuration). It also explains the rationale for dual failure counts. The missing piece is an explicit statement of output structure (e.g., 'returns a list of queues'), but it is strongly implied by the content.

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 coverage is 100%—each parameter already has a clear description (windowHours, includeInternal, stuckAfterMinutes). The tool description does not add any meaning or context about these parameters beyond the schema, so it stays at the baseline of 3.

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?

The description clearly states the verb+resource: 'Per-queue health snapshot' with specific metrics (job counts, stuck jobs, completions, failures, configuration). It distinguishes from siblings by scope and content, but does not explicitly name any sibling or contrast itself, so it doesn't fully earn 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 Guidelines2/5

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

No guidance on when to use this tool versus alternatives like diagnose or failed_jobs. The description implies a health overview but never states conditions like 'use for a quick queue-wide health check' or 'not for deep dives into individual job failures.' The agent must infer usage context.

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