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cgrdavies

mcp-clickhouse-long-running

by cgrdavies
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: list_databases for database enumeration, list_tables for table metadata within a database, and run_select_query for executing queries. There is no overlap in functionality, making tool selection straightforward for an agent.

    Naming Consistency5/5

    All tools follow a consistent verb_noun naming pattern (list_databases, list_tables, run_select_query), using snake_case throughout. This predictability aids in understanding and usage without any deviations.

    Tool Count3/5

    With only 3 tools, the server feels thin for a database interaction scope, lacking operations like data manipulation (INSERT/UPDATE/DELETE), schema modification, or query execution beyond SELECT. While core functions are present, the count is borderline low for comprehensive database management.

    Completeness2/5

    The tool set is significantly incomplete for ClickHouse database operations, covering only listing and SELECT queries. Missing are essential CRUD operations (e.g., INSERT, UPDATE, DELETE), schema changes (CREATE/ALTER/DROP), and other query types (e.g., SHOW, DESCRIBE), which will likely cause agent failures in broader workflows.

  • Average 3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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.

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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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but doesn't cover critical aspects like required permissions, potential impacts (e.g., read-only vs. mutations), rate limits, or response format. This is a significant gap for a database query 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 a single, efficient sentence with zero waste, clearly front-loading the core purpose. It's appropriately sized for the tool's complexity, making it easy to parse.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (database querying), lack of annotations, no output schema, and 0% schema coverage, the description is incomplete. It doesn't address behavioral traits, parameter details, or return values, leaving critical gaps for effective agent use.

    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?

    The schema description coverage is 0%, with one parameter 'query' undocumented in the schema. The description adds no details about parameter semantics, such as query syntax, supported SQL features, or constraints, failing to compensate for the coverage gap.

    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 action ('Run a SELECT query') and the target resource ('in a ClickHouse database'), which provides a specific verb+resource combination. However, it doesn't differentiate from sibling tools like 'list_databases' or 'list_tables' beyond the SELECT query specificity, keeping it from a perfect score.

    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 versus alternatives. It doesn't mention prerequisites, limitations, or comparisons to sibling tools, leaving the agent with no explicit usage context or exclusions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It describes what information is returned but doesn't mention performance characteristics, rate limits, authentication requirements, pagination behavior, or whether this is a read-only operation (though 'list' implies read-only). The description is functional but lacks important operational context.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, well-structured sentence that efficiently conveys the core functionality. It's appropriately sized for a listing tool and front-loads the essential information without unnecessary elaboration.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a tool with 3 parameters (0% schema coverage), no annotations, and no output schema, the description is insufficient. It explains what information is returned but doesn't address how to use the parameters, what format the output takes, or important behavioral considerations. The description leaves too many gaps given the tool's complexity and lack of structured documentation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the schema provides no parameter documentation. The description doesn't mention any parameters at all, failing to explain what 'database', 'like', or 'not_like' parameters do or how they affect the listing. This leaves significant gaps in understanding how to use the tool effectively.

    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 verb 'list' and resource 'ClickHouse tables in a database', specifying what information is included (schema, comment, row count, column count). It distinguishes from 'list_databases' by focusing on tables within a database, but doesn't explicitly differentiate from 'run_select_query' which could also retrieve table information.

    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 versus alternatives like 'run_select_query' for table information or 'list_databases' for database listing. It mentions what the tool does but offers no context about appropriate use cases or exclusions.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but doesn't cover key aspects like whether this is a read-only operation, if it requires specific permissions, potential rate limits, or what the output format looks like (e.g., list of names or detailed metadata).

    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 a single, clear sentence that front-loads the essential information without any wasted words. It's appropriately sized for a simple tool with no parameters.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. However, it lacks context about behavioral traits and usage guidelines, which are important even for simple tools to ensure correct agent invocation.

    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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't add unnecessary details, aligning with the lack of inputs.

    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 action ('List') and resource ('available ClickHouse databases'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_tables' beyond the resource name, missing an explicit distinction.

    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?

    No guidance is provided on when to use this tool versus alternatives like 'list_tables' or 'run_select_query'. The description lacks context about prerequisites, such as whether authentication is needed or if it's for exploration versus query execution.

    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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