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ClickHouse

mcp-clickhouse

Official
by ClickHouse
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

75%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion or overlap.

    Naming Consistency5/5

    With a single tool, naming is trivially consistent.

    Tool Count1/5

    A single 'run_query' tool is far too few for a ClickHouse server; users would expect multiple tools for schema management, data listing, etc.

    Completeness1/5

    The tool set is severely incomplete—no tools for exploring databases, tables, or performing any operation beyond raw SQL, which defeats the purpose of an MCP server.

  • Average 4.1/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 11 of 18 community issues answered or closed in the last 6 months
    • 11 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    With no annotations, the description fully bears the burden of behavioral disclosure. It correctly notes that queries are read-only by default and warns about destructive operations when enabled. This is valuable transparency, though additional details about execution limits or error handling could improve it.

    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 highly concise—one sentence with bullet-like details. It front-loads the core purpose and then efficiently states behavioral notes, with no redundant information.

    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 a simple query execution tool, the description covers the key behaviors (read-only vs. write modes) and implies the output schema exists. It does not mention return value structure, but the presence of an output schema likely fills that gap.

    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?

    There is only one parameter ('query'), and the description does not provide any detail beyond its name and type. Since schema description coverage is 0%, the description should compensate, but it does not explain query format, constraints, or examples.

    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's function: 'Execute SQL queries in ClickHouse.' The verb 'Execute' and resource 'SQL queries' are specific, distinguishing it from sibling tools that list databases or tables.

    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 explains the default read-only behavior and how to enable write operations via environment variables, providing clear context for different use cases. However, it does not explicitly state when to use this tool versus sibling tools.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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