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SnowLeopard-AI

BigQuery MCP

by SnowLeopard-AI
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.9

  • Disambiguation5/5

    Each tool has a distinct and clear purpose: query executes SQL, list_tables enumerates tables, and get_schema describes table structure. There is little risk of an agent selecting the wrong tool for a task.

    Naming Consistency4/5

    list_tables and get_schema follow a consistent verb_noun snake_case pattern. The lone tool 'query' is a simple verb without a noun object, which is a minor deviation but still clear and natural for the operation.

    Tool Count4/5

    Three tools is on the minimal side but reasonable for a focused BigQuery read/query server. Each tool serves a distinct need, though the server could support a slightly broader set without feeling bloated.

    Completeness4/5

    The query tool can execute arbitrary SQL, including DDL/DML, so most BigQuery operations are reachable indirectly. The main gaps are convenience features like listing datasets or managing query jobs, but agents can work around these with SQL and INFORMATION_SCHEMA queries.

  • Average 3.1/5 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • This repository is licensed under MIT License.

  • 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?

    No annotations exist, so the description carries the full burden of behavioral disclosure. It implies a read-only operation, but it does not explain what the schema includes, what happens for unknown tables, whether access is required, or what the response format will be.

    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 declarative sentence with no filler, and the core purpose is front-loaded. It is appropriately brief for a simple tool, though the brevity comes at the cost of missing behavioral and parameter context.

    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?

    For a one-parameter read-only tool, this is minimally viable: it states the operation and target. However, it omits return expectations and useful context such as linking to list_tables for discovering valid table names, leaving notable gaps for an agent to call it confidently.

    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?

    Schema description coverage is 0%, so the description needed to clarify the 'table' parameter. It only restates the concept of 'a given table' in English and adds no details about valid values, naming conventions, or how to discover available tables.

    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 a specific action ('Get') and resource ('the schema for a given table'), which distinguishes it in kind from siblings like query and list_tables. It does not explicitly contrast with siblings, but the resource type is evident enough.

    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?

    There is no explicit guidance on when to use this tool versus query or list_tables. The intended use is implied by the tool name and one-line description, but no prerequisites, exclusions, or alternative conditions are provided.

    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 of disclosing behavior. It states that SQL is executed and results are returned, but it does not say whether this is read-only, whether DDL/DML is permitted, whether side effects can occur, or what happens if the query is expensive or large. This is a significant gap for an arbitrary SQL execution 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, direct sentence that front-loads the action and result. There is no wasted wording or redundant detail.

    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?

    With no annotations and no output schema, the description needs to provide enough context for an agent to invoke the tool safely and correctly. For an arbitrary SQL execution tool, it omits important constraints: whether only SELECT is allowed, how results are returned, row limits, and potential cost or side-effect warnings. The description is minimally functional but not complete for this kind of operation.

    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?

    The input schema already documents the single 'sql' parameter with 100% coverage. The description essentially restates the schema field ('BigQuery sql statement'), adding little meaning beyond what the structured definition provides. Baseline 3 is appropriate.

    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 identifies the action ('Executes'), the resource ('BigQuery sql statement'), and the outcome ('returns the results'). It is unambiguous, though it does not explicitly differentiate itself from sibling tools list_tables and get_schema, which are also BigQuery-related.

    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 given about when to use this tool versus list_tables or get_schema. An agent is not told that query should be used for arbitrary SQL while the siblings cover metadata or schema access, nor are any exclusions or prerequisites stated.

    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, the description carries the full burden of behavioral disclosure. It only says 'List the tables available' — no mention of whether system tables are included, the return format, pagination, or authentication requirements. The word 'list' implies read-only, but scope is vague.

    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?

    A single, front-loaded sentence with no filler. Every word contributes to the purpose, making it appropriately concise for a zero-parameter tool.

    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?

    For a simple list tool with no parameters or output schema, this is minimally adequate. However, it doesn't clarify what 'available' means (e.g., current schema, user permissions), the return shape, or how it relates to siblings — leaving a small but notable gap.

    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 zero parameters, so the schema fully documents the input surface. The description doesn't need to add parameter semantics; baseline of 4 applies.

    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 a specific verb ('List') and resource ('tables'), and the phrase 'available' conveys scope. Even without naming siblings, the operation is obviously distinct from 'query' (data retrieval) and 'get_schema' (schema inspection).

    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 gives no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, whether to call this before query/get_schema, or any context indicating selection criteria.

    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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