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clearskies-py

clearskies MCP Server

by clearskies-py
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Python

list_available_validators

List all available clearskies validator types with a short description of each.

Instructions

List all available clearskies validator types with a short description of each.

Validators are used to validate model data before saving. They can be attached
to columns via the validators parameter.

Example:
    from clearskies.validators import MinimumLength, Required

    name = columns.String(validators=[Required(), MinimumLength(3)])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.2

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It signals a read-only enumeration whose entries include short descriptions, but says nothing about result ordering, size, or whether the catalogue is environment-specific. For a zero-parameter catalogue tool this is adequate but thin.

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?

Front-loaded with the core action in the first sentence, then a usage note and a compact code example. The example earns its place by showing the import path and attachment syntax, though it is slightly beyond the minimum needed to call a listing tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 not be described, and the example covers the immediate follow-on use of the listed validators. Complete for a simple catalogue tool, with only the list-vs-detail routing left unstated.

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

Parameters4/5

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

The tool takes no parameters, so the baseline is 4. Schema coverage is 100% and there is nothing for the description to compensate for.

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?

'List all available clearskies validator types with a short description of each' gives a clear verb and resource. It implicitly contrasts with get_validator_info (full detail) by promising only a short description, but does not name the sibling explicitly, so it falls short of top-tier differentiation.

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?

The description explains what validators ARE and how they attach to columns, but this is domain background, not tool-selection guidance. It never says when to call list_available_validators versus get_validator_info or list_available_columns, and offers no exclusions or prerequisites.

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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