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paulieb89

UK Business Tools - Ledgerhall

by paulieb89

law_get_prompt

Read-onlyIdempotent

Retrieve a named prompt with optional arguments, returning rendered prompt messages as JSON. Ideal for dynamically fetching pre-configured prompts to streamline automated workflows and ensure consistent output.

Instructions

Get a prompt by name with optional arguments.

Returns the rendered prompt as JSON with a messages array. Arguments should be provided as a dict mapping argument names to values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the prompt to get
argumentsNoOptional arguments for the prompt

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context by stating that it returns a rendered prompt as JSON with a messages array and that arguments should be a dict mapping names to values, which goes beyond the annotations without contradicting them.

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 only two sentences long, front-loaded with the core action, and contains no filler. Every sentence provides value: the first states the purpose, the second clarifies output and argument format. Excellent conciseness.

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?

Given the tool's simplicity, strong annotations, and the presence of an output schema, the description sufficiently covers the key behavioral aspects: what it does, how arguments are passed, and the output shape. No critical information is missing for an agent to select and invoke the tool correctly.

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

Parameters3/5

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

Schema description coverage is 100% for both parameters, so the schema already documents each parameter. The description reinforces the arguments format ('dict mapping argument names to values') but adds little beyond what the schema provides. Baseline 3 is appropriate.

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 clearly states 'Get a prompt by name' with a specific verb and resource, making the core purpose unambiguous. However, it does not explicitly differentiate itself from sibling tools like 'law_list_prompts' or the similarly named 'dd_get_prompt', so it falls short of a 5.

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 usage: use this tool when you have a prompt name and want the rendered prompt. There is no explicit mention of alternatives or when not to use it, such as pointing to 'law_list_prompts' for discovery, so guidance is only implied.

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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