tables_sample_tablePreview sample rows from a Snowflake table to inspect data before querying; optionally set the sample size (default 10).
Preview sample rows from a table (default 10 rows).
| Name | Required | Description | Default |
|---|---|---|---|
| table_name | Yes | Quote names that aren't all uppercase, exactly as list tools return them. | |
| sample_size | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v3.0.0Input schema / properties / table_name / descriptionAdded value: +"Quote names that aren't all uppercase, exactly as list tools return them."v2.0.4Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, open-world behavior, so the safety profile is fully covered. The only added detail is 'default 10 rows', which merely restates the schema default; nothing is said about sampling method, ordering, cost on very large tables, or whether a sample call can be expensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler, and the key qualifier (default row count) is in parentheses where it belongs. It is efficient but borders on under-specified given the large tool surface around it.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return shape need not be described. Still, for a sampling tool the description omits determinism of the sample, ordering, behavior on tables with fewer rows than sample_size, and how it relates to the sibling profiling/inspection recipes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50%: table_name carries a precise quoting rule while sample_size has only a default. The description restates that default (10 rows) but adds no guidance on acceptable ranges, upper bounds, or what happens for small tables; the naming/quoting semantics live entirely in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Preview sample rows from a table') plus the default row count, so the operation is unambiguous. It does not distinguish it from near-siblings like tables_describe_table or recipes_inspect_table_with_sample, leaving the agent to infer the difference.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The word 'Preview' implies a light exploratory use, but there is no explicit when-to-use, no exclusions, and no named alternative among the many sibling tools that also read table data. An agent must guess whether to sample, describe, get DDL, or profile.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.