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Snowflake List Streams

snowflake_list_streams
Read-only

List table and view streams in a Snowflake database or schema to inspect change tracking. Filter by name pattern for targeted results.

Instructions

List table/view streams in a database or schema.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
patternNo
databaseNo
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 / pattern / title
      Removed value: -"Pattern"
    • removedInput schema / properties / schema_name / title
      Removed value: -"Schema Name"
    • removedInput schema / title
      Removed value: -"snowflake_list_streamsArguments"
    • removedOutput schema / title
      Removed value: -"snowflake_list_streamsDictOutput"
  2. First observedv0.1.0

TDQS

C2.9/5.0
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 only the scoping constraint (database or schema) and says nothing about filtering behavior, result limits, or pagination, so it stays at baseline for an annotated read-only tool.

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?

A single compact sentence with the verb and scope front-loaded and zero filler. It is concise, though the brevity tips into under-specification rather than sharpness.

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?

An output schema exists so return values need no explanation, but with three undocumented parameters at 0% coverage and no usage or filtering guidance, an agent cannot reliably call this tool with a `pattern` filter or know what scoping it inherits when both database and schema_name are omitted.

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?

All three parameters (pattern, database, schema_name) have 0% schema description coverage, so the description must carry the load. It hints at database/schema scoping but leaves `pattern` completely unexplained (is it a LIKE pattern on stream names? case-sensitive?), which is the parameter most likely to be misused.

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 (List) and resource (table/view streams) plus the scope (a database or schema). It is distinguishable from list_tables/list_views, but does not explicitly contrast with the closest siblings such as snowflake_describe_stream or snowflake_list_dynamic_tables.

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 when-to-use guidance, no prerequisites, and no mention of alternative tools (describe_stream, create_stream, read_stream_changes) or when to prefer a database-wide vs schema-wide listing. The scope wording implies usage but nothing is stated.

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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