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io.github.Aguantar/clickhouse-dataops-mcp

io.github.Aguantar/clickhouse-dataops-mcp

by Aguantar
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ch_table_schema

Retrieve comprehensive table metadata including columns, engine, keys, partitions, and sample data to understand table structure before writing queries.

Instructions

Get comprehensive table metadata: columns, engine, keys, partitions, and sample data.

Returns column types, partition/sorting/primary keys, TTL settings, row counts, disk usage, partition breakdown, and 5 sample rows. Essential for understanding table structure before writing queries.

Args: table: Table name database: Database name (default: cdc_pipeline)

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": {
      "table": "<table>"
    },
    "name": "ch_table_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault
tableYes
databaseNocdc_pipeline

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It clearly lists what the tool returns (column types, partition keys, row counts, disk usage, sample rows), implying it is a read-only operation. It does not mention any side effects or authorization requirements, which would be beneficial but is not critical for this read-like tool.

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 concise with three sentences plus an Arg list. It front-loads the main purpose, then lists specifics, and ends with the parameters. Every sentence adds value, and the structure is easy to parse.

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 that there is an output schema (indicated by context), the description sufficiently covers the tool's functionality and return values (e.g., column types, partition keys, sample rows). It includes enough detail for an agent to understand when and how to use it effectively.

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?

The input schema has no descriptions (0% coverage), but the description adds an 'Args' section explaining 'table: Table name' and 'database: Database name (default: cdc_pipeline)'. This provides meaning beyond the bare schema definitions and clarifies the default for the optional parameter.

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 tool is explicitly described as getting comprehensive table metadata, listing specific items like columns, engine, keys, and sample data. It is distinct from sibling tools like ch_list_tables (list tables) and ch_query (run queries), which deal with table listing and data retrieval respectively.

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 states it is 'Essential for understanding table structure before writing queries,' providing clear context for use. It also notes the default database. However, it does not explicitly mention when not to use it or point to alternatives for other schema-related tasks.

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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