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clearskies-py

clearskies MCP Server

get_validator_info

Access comprehensive docs for any clearskies validator type. Supply the validator name (e.g., 'Required', 'Email') to see its usage, parameters, and code examples.

Instructions

Get detailed documentation for a specific clearskies validator type.

Args:
    validator_type: The validator type name (e.g. "Required", "Unique", "Email").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
validator_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It correctly implies this is a read-only documentation lookup, but it does not disclose error behavior, required permissions, or what happens if an invalid validator type is provided. For a simple information retrieval tool, this is adequate but leaves some gaps.

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 description is two concise sentences plus a brief argument explanation. It is front-loaded with the main purpose, contains no fluff, and every sentence contributes useful information.

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?

Given this is a simple single-parameter tool with an output schema, the description is mostly complete. However, it could be enhanced by referencing the sibling list_available_validators as a source for valid validator_type values, but this is not essential for basic usage.

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 input schema has 0% description coverage, but the tool description compensates by explaining that validator_type is 'the validator type name' and provides concrete examples ('Required', 'Unique', 'Email'). This adds meaning beyond the schema's bare type definition, though it could further specify case sensitivity or valid formats.

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 clearly states the tool's action ('Get detailed documentation') and resource ('a specific clearskies validator type'). It distinguishes from sibling tools like list_available_validators by emphasizing 'specific' and providing example validator names, making the scope unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this tool is used when you need documentation for a specific known validator type, but it does not explicitly state when to use it versus alternatives, nor does it mention exclusions or prerequisites. No sibling tool or 'when not to use' guidance is provided.

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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