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clearskies-py

clearskies MCP Server

get_column_info

Access comprehensive documentation and constructor details for a specific clearskies column type by providing its name.

Instructions

Get detailed documentation and constructor parameters for a specific clearskies column type.

Args:
    column_type: The name of the column type (e.g. "String", "Integer", "BelongsToId").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
column_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries the burden. It conveys that the tool retrieves documentation and constructor parameters, implying a non-mutating read operation. However, it does not disclose error behavior (e.g., invalid column_type) or any additional details, but for a simple getter this is adequate.

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 sentences long, front-loaded with the main purpose, and uses a clear Args block for parameter details. There is no redundant text while still providing essential examples.

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?

For a single-parameter info-retrieval tool with an output schema, the description gives sufficient context about what the tool does and what input to provide. It doesn't explain return values, but the output schema handles that. Minor gaps exist around error handling, but given the tool's simplicity, it is reasonably complete.

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?

Although schema description coverage is 0%, the description compensates with a dedicated Args section that explains column_type as 'the name of the column type' and provides concrete examples ('String', 'Integer', 'BelongsToId'). This fully clarifies the parameter's meaning and expected format.

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 a specific action ('Get detailed documentation and constructor parameters') and resource ('a specific clearskies column type'), distinguishing it from sibling list tools like list_available_columns. The verb and resource are explicit, leaving no ambiguity about what the tool does.

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

Usage Guidelines4/5

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

The description provides clear context: use when you need documentation or constructor parameters for a named column type. It does not explicitly mention alternatives or exclusions, but the focus on 'specific' versus the listing siblings makes the usage context obvious.

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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