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Aguantar

io.github.Aguantar/clickhouse-dataops-mcp

by Aguantar

ch_disk_usage

Analyze disk usage by table and partition, providing per-table breakdown, TTL information, and recommendations for optimization.

Instructions

Analyze disk usage by table and partition with recommendations.

Returns:

  • Total disk usage for the database

  • Per-table breakdown (rows, size, parts, percentage)

  • Per-partition breakdown (top 20 by size)

  • TTL information

  • Recommendations (excessive parts, missing TTL on large tables)

Args: database: Database to analyze (default: cdc_pipeline)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
databaseNocdc_pipeline

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must fully disclose behavior. It lists specific outputs (total usage, per-table breakdown, etc.) and implies it is a read-only analysis (no side effects mentioned). However, it does not explicitly state that it does not modify data or require special permissions. The description is adequate but leaves some ambiguity.

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 very concise, with a clear separation between the action statement, return values, and parameters. Every sentence adds value, and the bullet list for returns is easy to parse. No unnecessary words.

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?

Given the tool's complexity (disk usage analysis with multiple outputs) and the presence of an output schema, the description covers the main points: total usage, per-table and per-partition breakdowns, TTL info, and recommendations. It does not explain edge cases or detailed interpretation of recommendations, but these may be handled by the output schema. Overall, it is sufficient for its purpose.

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?

The single parameter 'database' is fully described in the Args section, including its purpose and default value. This adds meaning beyond the input schema, which has no parameter descriptions (schema coverage 0%). The explanation is clear and complete.

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 tool analyzes disk usage by table and partition with recommendations. It specifies a concrete verb ('analyze') and resource ('disk usage'), which distinguishes it from sibling tools like ch_data_quality or ch_query. However, it could be more explicit about the scope (e.g., 'of the ClickHouse database').

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?

The description provides no guidance on when to use this tool vs. alternatives (e.g., ch_list_tables or ch_slow_queries). It does not mention prerequisites, configuration, or contexts where other tools would be preferred.

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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