Dryos

List a column's values

column_values

Read-onlyIdempotent

The distinct values of one text column, most rows first — what a filter can be.

Args:
    slug: The stream.
    column: A text column from describe_stream (not the entity — use find_entities).
    where: Filters as `column=value`, the same as query_stream's.
    search: `column:text`, a case-insensitive substring.
    limit: At most this many (1–500).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`slug`	Yes
`limit`	No
`where`	No
`column`	Yes
`search`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 18, 2026

TDQS

A5/5.0

Behavior5/5

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

Annotations already declare the operation read-only, idempotent, and non-destructive. The description adds concrete behavior beyond those hints: values are distinct, ordered by frequency, search is a case-insensitive substring, and limits are constrained to 1–500. It also signals the output can be used as filter values, which is useful for downstream planning.

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?

The purpose is front-loaded in a single line, followed by a tight argument list where every line adds necessary information. There is no filler or repetition of schema defaults.

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?

For a read-only listing tool with an output schema, this is complete: all parameters are covered, safety is fully disclosed by annotations, and cross-tool references fill in syntax details. An agent can invoke it correctly without additional lookup.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden, and it succeeds. Every parameter gets a meaningful explanation: slug is the stream, column must be a text column from describe_stream, where follows query_stream syntax, search takes column:text format, and limit is bounded 1–500.

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 states a specific operation: listing the distinct values of one text column, ordered by frequency. It also scopes the resource precisely by saying 'one text column' and 'most rows first', which distinguishes it from generic stream/list tools. The title and description align without tautology.

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?

It gives explicit routing guidance: columns must come from describe_stream, entities should use find_entities instead, and where syntax matches query_stream's. This tells an agent when to use this tool and which siblings to use for adjacent cases.

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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