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sfc-gh-tjia

Snowflake MCP Server

by sfc-gh-tjia
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose: executing SQL queries on Snowflake.

    Naming Consistency5/5

    The single tool name 'execute_snowflake_sql_query' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect with no deviations or mixed conventions.

    Tool Count2/5

    A single tool for a Snowflake server feels too thin for the apparent scope, as it only provides raw SQL execution without any higher-level operations like listing tables, managing schemas, or handling data workflows. This is a borderline case leaning toward under-scoped.

    Completeness2/5

    The tool surface is severely incomplete for a Snowflake domain, lacking CRUD/lifecycle coverage. There are significant gaps such as no tools for browsing databases, tables, or schemas, and no support for common operations like data loading or user management, which will likely cause agent failures.

  • Average 3.8/5 across 1 of 1 tools scored.

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

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively warns about security risks, permissions, and network considerations, and mentions that it returns formatted results or error messages. However, it lacks details on rate limits, query execution limits, or specific error handling, which holds it back from a perfect score.

    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 well-structured with a clear purpose statement, security warning, and parameter/return sections. It is appropriately sized and front-loaded, but the security warning could be more concise, and some sentences are slightly verbose.

    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 complexity of executing SQL queries and the lack of annotations, the description does a good job covering purpose, security, parameters, and returns. With an output schema present, it doesn't need to detail return values, but it could benefit from more context on error scenarios or performance implications.

    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 schema description coverage is 0%, so the description must compensate. It lists all four parameters with brief explanations (e.g., 'override the default'), adding meaningful context beyond the schema. However, it does not provide examples or detailed constraints, such as valid formats for database names, preventing a score of 5.

    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 tool's purpose as 'Execute any SQL query on Snowflake,' which specifies the verb (execute) and resource (SQL query on Snowflake). However, since there are no sibling tools, it cannot differentiate from alternatives, preventing a score of 5.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description includes a security warning that implies usage in secure contexts and suggests considering restrictions for production, but it does not explicitly state when to use this tool versus alternatives or provide clear exclusions. The guidance is implied rather than explicit.

    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.
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  • Evaluate tool definition quality.

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