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baburajr

elasticsearch-mcp

by baburajr

shard_allocation

Read-only

Diagnose unassigned shards and resolve yellow or red cluster health by getting per-node disk usage and decider-level reasons such as disk watermark, awareness, filtering, or max_retries.

Instructions

Shard placement and why shards are unassigned. Returns _cat/shards, per-node disk usage, and for any UNASSIGNED shard the cluster allocation explanation (decider-level reasons: disk watermark, awareness, filtering, max_retries). This is the tool for a yellow/red cluster.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
indexNo*
only_problemsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations already establish read-only and non-destructive behavior. The description adds valuable detail beyond that, specifying the exact outputs: _cat/shards, per-node disk usage, per-node usage, and decider-level allocation explanations for unassigned shards. This helps an agent understand what to expect without excelling the documented safety profile.

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 three sentences long, front-loads the core purpose, and every sentence adds useful detail. It includes concrete output categories and the key use case without unnecessary fluff.

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 an annotated read-only diagnostic tool with an output schema, the description provides strong context and enough detail about outputs. The missing explicit handling of the parameters is the only notable gap, but the output schema and self-explanatory parameter names keep this mostly complete.

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?

Schema description coverage is 0% and the description does not explain the 'index' or 'only_problems' parameters. While the parameter names are somewhat self-explanatory and default values are in the schema, the description does not carry the weight needed to compensate for the missing semantic documentation.

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 what the tool does: reports shard placement, why shards are unassigned, and cluster allocation decisions. It differentiates itself by specifically targeting the yellow/red cluster diagnostic scenario, which helps separate it from broader health tools like cluster_health or index_health.

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 explicitly gives a usage context: 'This is the tool for a yellow/red cluster.' This gives a clear when-to-use signal, though it does not explicitly contrast this tool with alternatives such as cluster_health or explain_query.

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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