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OliveriGuido

Databricks MCP Server

by OliveriGuido
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no risk of confusion between tools. The tool's purpose is clearly defined as running a read-only SQL query on a specific table.

    Naming Consistency5/5

    With a single tool, naming consistency is not a concern. The tool name 'run_query' follows a common verb_noun pattern.

    Tool Count1/5

    The server is named 'Databricks MCP Server', implying access to a wide range of Databricks functionality, but only one tool for a single query on a fixed table is provided. This is an extreme mismatch in scope.

    Completeness1/5

    The tool set is severely incomplete for a Databricks server. It lacks operations for managing databases, tables, clusters, or running arbitrary SQL beyond the fixed sample table.

  • Average 4.4/5 across 1 of 1 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

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  • 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

  • Behavior4/5

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

    Given no annotations, the description reveals read-only behavior, query type restrictions, and automatic LIMIT. It does not cover error handling or response format, but core behaviors are disclosed.

    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?

    Two concise sentences: first states purpose, second adds constraints. No superfluous words, front-loaded structure.

    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?

    With low complexity (1 param) and an output schema present, the description covers essential context: target table, read-only, and query limitations. Minor gaps like error handling are acceptable.

    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?

    Schema coverage is 0%, so description must compensate. It adds context by specifying the target table and query constraints, which adds meaning beyond the bare parameter name.

    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 verb 'Run a READ-ONLY SQL query' and the specific resource 'samples.nyctaxi.trips', with explicit read-only nature and allowed query types. This fully defines the tool's purpose.

    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?

    It provides constraints like 'Only a single SELECT/WITH is allowed' and 'LIMIT 1000 is appended if missing', guiding usage. No alternatives are discussed, but no siblings exist, so it's adequate.

    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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Glama performs regular codebase and documentation scans to:

  • 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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