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hydrolix

mcp-hydrolix

Official
by hydrolix
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Python
Remote

Get Table Info

get_table_info
Read-onlyIdempotent

Retrieve table metadata including columns, summary detection, and merge functions to prevent query errors.

Instructions

Get detailed metadata for a specific table including columns and summary table detection.

REQUIRED USAGE: Call this tool BEFORE querying ANY table to check if it's a summary table and get column metadata. This is mandatory to avoid query errors.

This tool provides:

  • is_summary_table: Boolean indicating if table has pre-aggregated data

  • columns: List of column objects, each with a column_category field:

    • column_category='Column': plain dimension column

    • column_category='AliasColumn': non-aggregate ALIAS column, has default_expr

    • column_category='AggregateColumn': AggregateFunction/SimpleAggregateFunction type, has base_function and merge_function

    • column_category='SummaryColumn': ALIAS column that transitively depends on aggregates, has default_expr

  • summary_table_info: Human-readable description for summary tables

  • total_rows, total_bytes: Table statistics

WORKFLOW for querying tables:

  1. Call get_table_info('database', 'table_name')

  2. Check is_summary_table field

  3. If is_summary_table=True:

    • Read column_category and merge_function for each column

    • Use merge_function to wrap aggregate columns in queries

    • Example: SELECT countMerge(count(vendor_id)) FROM table

  4. If is_summary_table=False:

    • Use standard SQL (SELECT count(*), sum(col), etc.)

  5. Execute query with run_select_query

For summary tables, aggregate columns MUST be wrapped with their corresponding -Merge functions from the merge_function field. Querying without checking this metadata first will cause errors.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
databaseYes
tableYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds valuable behavioral details like column categories (Column, AliasColumn, AggregateColumn, SummaryColumn), merge_function usage, and the distinction between summary and non-summary tables, which beyond annotations.

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, bullet points for return fields, and a numbered workflow. However, it is slightly verbose, repeating the workflow in both narrative and list form. Front-loaded with the essential usage instruction.

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

Completeness5/5

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

Despite the lack of parameter descriptions, the description fully covers the tool's purpose, return fields (including column categories and merge_function), and the complete workflow for using the tool alongside run_select_query. Given the output schema exists, return values are adequately explained.

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 input schema has two required parameters (database, table) with no descriptions (0% coverage). The description only mentions 'Call get_table_info('database', 'table_name')' but does not define what database or table mean, leaving the agent to infer from context. This is insufficient compensation for the missing schema documentation.

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 'Get detailed metadata for a specific table including columns and summary table detection.' It uses a specific verb ('Get') and resource ('table metadata'), and the sibling tools (list_databases, list_tables, run_select_query) are distinct, so there is no confusion.

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

Usage Guidelines5/5

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

The description explicitly mandates 'Call this tool BEFORE querying ANY table' and provides a numbered workflow detailing when and how to use it. It also implicitly guides when not to use it (after metadata is obtained) by linking to run_select_query.

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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