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peterbedorjr

orca-copilot-mcp

by peterbedorjr

get_preset

Inspect a preset's inheritance chain and its own overrides, or resolve selected settings to effective values along the chain.

Instructions

Read a preset: its inherits chain and the settings it overrides itself.

Pass keys to additionally resolve specific settings to their effective values, each with the preset in the chain that supplies it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keysNo
nameYes
preset_typeYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It clearly indicates a read-only operation and describes the output contents, including effective-value resolution. It does not mention error behavior, unknown keys, or what happens when keys is null, so some behavioral ambiguity remains.

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 compact and front-loaded: the core purpose appears in the first line, and the optional parameter behavior adds only what is needed. Every sentence earns its place with no filler or repetition.

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

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description gives a solid high-level picture of what the tool returns and how the optional parameter changes behavior. However, without an output schema it does not disclose the exact return structure, and it leaves the required parameters underspecified, so an agent may still need to guess about preset_type values or result formatting.

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?

Schema description coverage is 0%, so the description must compensate. It explains the optional `keys` parameter well: it resolves specific settings to effective values and identifies the supplying preset. However, it gives no semantic detail for the required parameters `preset_type` and `name`, leaving their possible values and relationship to the preset unclear.

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?

The description states a clear verb ('Read') and resource ('a preset'), and specifies exactly what is returned: the inherits chain and the settings the preset overrides itself. This distinguishes it from sibling tools like list_presets and set_preset_values, though it does not explicitly name alternatives.

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 second sentence gives concrete guidance for optional use: 'Pass keys to additionally resolve specific settings to their effective values.' However, there is no explicit guidance about when to choose this tool over siblings such as list_presets or get_current_presets; usage is implied rather than 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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