io.github.Aguantar/clickhouse-dataops-mcp

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ch_query

Run read-only SELECT queries on ClickHouse with automatic safety checks, LIMIT enforcement, and partition pruning analysis to optimize performance.

Instructions

Execute a read-only SQL query against ClickHouse.

Runs SELECT queries with automatic safety validation (DDL/DML blocked), LIMIT enforcement, and partition pruning analysis. Returns results with a warning if the query doesn't leverage partitioning efficiently.

Args: sql: SELECT query to execute database: Target database (default: cdc_pipeline) max_rows: Maximum rows to return (default: 1000, max: 10000)

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": {
      "sql": "<sql>"
    },
    "name": "ch_query"
  }
}

Input Schema

Name	Required	Default
`sql`	Yes
`database`	No	cdc_pipeline
`max_rows`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Jun 3, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses automatic safety validation, LIMIT enforcement, partition pruning analysis, and a warning for inefficient queries. It also specifies default values for database and max_rows. Could mention error handling 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 concise and well-structured: a brief purpose statement, a paragraph of key features, and a clear Args section. Every sentence adds value without redundancy.

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 output schema exists, the description adequately covers query execution, validation, and limits. It could be more complete by summarizing the response format, but the main aspects are covered.

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?

Parameter descriptions add value beyond the bare schema: 'SELECT query to execute' for sql, target database with default for database, and maximum rows with default and max for max_rows. This compensates for the 0% schema description coverage.

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 the tool 'Execute a read-only SQL query against ClickHouse', specifying the verb (execute), resource (ClickHouse), and nature (read-only). It distinguishes itself from potential write operations by emphasizing 'read-only' and 'SELECT queries'.

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 limits usage to SELECT queries with automatic safety validation blocking DDL/DML, and mentions LIMIT enforcement. It does not explicitly compare with sibling tools like ch_explain_query or ch_table_schema, but provides clear context on when to use this tool for general querying.

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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