snowflake_list_event_tablesLists Snowflake event tables used for application logging, tracing, and SPCS telemetry, helping you locate telemetry data by database, schema, or name pattern.
List event tables used for application logging, tracing, and SPCS telemetry.
| Name | Required | Description | Default |
|---|---|---|---|
| pattern | No | ||
| database | No | ||
| schema_name | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.2.0Input schema / additionalPropertiesAdded value: +falseInput schema / properties / database / titleRemoved value: -"Database"Input schema / properties / pattern / titleRemoved value: -"Pattern"Input schema / properties / schema_name / titleRemoved value: -"Schema Name"Input schema / titleRemoved value: -"snowflake_list_event_tablesArguments"Output schema / titleRemoved value: -"snowflake_list_event_tablesDictOutput"v1.1.5Does 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 clear. The description adds the scoping to event/logging/telemetry tables, which is useful context, but says nothing about result format, filtering behavior, or account/region scope. Adequate against the annotation baseline.
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 sentence with zero filler, front-loading the verb and resource. Nothing is wasted.
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 values needn't be explained. However, with three completely undocumented parameters and no usage or filtering guidance, the description is too thin for a tool whose parameters govern its behavior. It needs at least hints on how pattern/database/schema_name scope the listing.
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 description coverage is 0% – none of the three parameters (pattern, database, schema_name) are documented in the schema. The description does not mention any of them, so it fails to compensate for the coverage gap. An agent must guess at the syntax and semantics of pattern/database/schema_name.
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 ('List') and resource ('event tables'), and narrows scope to tables used for application logging, tracing, and SPCS telemetry. It is distinguishable from snowflake_list_tables and snowflake_list_iceberg_tables by the 'event tables' qualifier, though it doesn't explicitly contrast against those siblings.
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?
No when-to-use or when-not-to-use guidance is given. An agent cannot tell from the description when this tool should be preferred over snowflake_list_tables or snowflake_list_iceberg_tables, nor what preconditions apply.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.