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io.github.AIops-tools/ceph-aiops

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Python
Remote

pg_dump_stuck

Detect stuck Ceph placement groups and return implicated OSDs to investigate when a PG is not recovering.

Instructions

[READ] Stuck PGs (inactive/unclean/stale/undersized/degraded) with implicated OSDs.

Use this when a PG is not recovering — it surfaces the OSD ids to investigate. Returns {"stuck": [...], "returned": N, "limit": L, "truncated": bool}; when truncated is true there are more stuck PGs than were returned.

Args: target: Ceph target name from config; omit for the default. limit: Maximum stuck-PG rows to return. Default 200.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
targetNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.11.5
    • addedInput schema / properties / limit
      Added value: +{
+  "default": 200,
+  "title": "Limit",
+  "type": "integer"
+}
  2. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly marks the tool as read-only with '[READ]' and documents the exact return shape, including the truncated flag semantics. This goes beyond a minimal description, though it does not mention error cases or performance implications.

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 compact and well-structured: it opens with the tool's purpose, then gives usage guidance, return format in a code block, and parameter explanations. Every sentence adds value and there is no redundant content.

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?

Given there is no output schema, the description thoroughly explains the return value, including the truncated flag and limit field. It also covers both parameters and their defaults. For a two-parameter diagnostic tool, this is complete and leaves no critical gap for an AI agent.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must explain all parameters. It does so completely: target is described as a Ceph target name from config with an omit-for-default instruction, and limit is described as the maximum stuck-PG rows to return with its default. This is exactly the semantic information an agent needs to call the tool correctly.

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 and resource: it dumps stuck PGs (inactive/unclean/stale/undersized/degraded) and surfaces implicated OSDs. This clearly distinguishes it from sibling tools like pg_summary or cluster_health by focusing on stuck PG diagnosis. The '[READ]' prefix also signals the operation type.

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?

The description gives an explicit trigger condition: 'Use this when a PG is not recovering — it surfaces the OSD ids to investigate.' This tells the agent when to invoke the tool. It does not explicitly name alternatives or exclusions, but the context is clear enough to route correctly.

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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