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cloudera

Cloudera Iceberg MCP Server

Official
by cloudera
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Python
Remote

Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: execute_query runs SQL queries and returns results, while get_schema retrieves metadata about table names. There is no overlap in functionality or ambiguity about when to use each tool.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (execute_query, get_schema) with clear action-object relationships. The naming style is uniform throughout the set.

    Tool Count2/5

    With only 2 tools for a database/query server, the surface feels severely limited. While the tools cover basic query execution and schema inspection, typical database operations like table creation, data manipulation, or metadata exploration beyond table names are missing, making this feel under-scoped.

    Completeness2/5

    For an Impala/Cloudera Iceberg database server, the toolset is significantly incomplete. There are no tools for creating/dropping tables, inserting/updating data, managing partitions, or accessing detailed schema information beyond table names. This creates dead ends for agents trying to perform common database operations.

  • Average 3.4/5 across 2 of 2 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.

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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 full burden for behavioral disclosure. It mentions the return format ('as JSON') which is helpful, but doesn't address critical behavioral aspects like authentication requirements, query execution limits, error handling, whether this is read-only or can modify data, or performance characteristics. For a database query tool with zero annotation coverage, this is insufficient.

    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 extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place: 'Execute' (action), 'SQL query' (what), 'on the Impala database' (where), 'and return results as JSON' (output format). No wasted words or redundancy.

    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 database query execution tool with no annotations, no output schema, and minimal parameter documentation, the description is incomplete. It doesn't address safety considerations (read vs write operations), authentication needs, error scenarios, or result formatting details beyond 'JSON'. The agent would need to make assumptions about many critical aspects of tool behavior.

    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 elaborate on the 'query' parameter beyond what's implied by the tool name. It doesn't specify SQL dialect, query limitations, or parameter formatting requirements. However, with only one parameter, the baseline expectation is lower, and the description at least confirms this is a SQL query parameter.

    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 ('Execute a SQL query') and target resource ('on the Impala database'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'get_schema' - both could involve database operations, so the distinction isn't explicit.

    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 about when to use this tool versus alternatives. There's no mention of the sibling tool 'get_schema' or any context about when direct query execution is appropriate versus schema inspection. The description simply states what the tool does without usage context.

    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?

    No annotations are provided, so the description carries the full burden. It describes the basic operation but lacks behavioral details such as whether this requires specific permissions, how results are formatted (e.g., list, JSON), if there are rate limits, or if it's cached. For a tool with zero annotation coverage, this is a significant gap.

    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 efficiently conveys the tool's purpose without any wasted words. It is appropriately sized and front-loaded with the essential information.

    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 adequate but incomplete. It lacks details on behavioral aspects like return format or error handling, which are important for a tool with no structured output schema to guide the agent.

    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 omits parameter details, earning a baseline score of 4 for not adding unnecessary information.

    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 specific action ('Retrieve') and resource ('list of table names in the current Impala database'), distinguishing it from the sibling tool 'execute_query' which presumably runs queries rather than listing metadata.

    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 implies usage context ('current Impala database') but does not explicitly state when to use this tool versus alternatives like querying system tables directly or using other metadata tools. No exclusions or prerequisites are mentioned.

    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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