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Snowflake Describe Table

snowflake_describe_table
Read-only

Retrieve column definitions, data types, nullability, and primary keys for a Snowflake table or view to inspect schema before writing queries.

Instructions

Describe column definitions, data types, nullability, and primary keys for a table or view.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
databaseNo
table_nameYes
schema_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv1.2.0
    • addedInput schema / additionalProperties
      Added value: +false
    • removedInput schema / properties / database / title
      Removed value: -"Database"
    • removedInput schema / properties / schema_name / title
      Removed value: -"Schema Name"
    • removedInput schema / properties / table_name / title
      Removed value: -"Table Name"
    • removedInput schema / title
      Removed value: -"snowflake_describe_tableArguments"
    • removedOutput schema / title
      Removed value: -"snowflake_describe_tableDictOutput"
  2. First observedv0.1.0

TDQS

B3.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds which attributes are returned, which is useful, but says nothing about error behavior when the object does not exist or how unqualified names are resolved, and an output schema already exists for return shape.

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 element (columns, types, nullability, primary keys) is informative and nothing is repeated from the schema or annotations.

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?

The existence of an output schema means return values need no further explanation, and the scope ('table or view') is stated. However, for a three-parameter tool with zero schema description coverage, the omission of name-resolution semantics leaves a real gap.

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% for all three parameters (database, table_name, schema_name), so the description must compensate and does not. It never explains how the optional database/schema_name interact with table_name or whether the current session context is used as a default.

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?

States a specific verb (describe) and resource (table or view) plus the exact metadata returned: column definitions, data types, nullability, primary keys. That distinguishes it implicitly from list_tables and get_table_ddl, but no sibling is named and the view/table overlap with siblings like describe_iceberg_table is left for the agent to infer.

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 when-to-use guidance and no routing to alternatives such as get_table_ddl (full DDL), sample_table, or profile_table, which an agent could easily confuse with this tool. Usage is only implied by the word 'describe'.

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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