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get_prompt

Retrieve a named prompt with optional arguments, returning the rendered prompt as a JSON messages array.

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
Behavior3/5

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

No annotations are provided, so the description carries the burden. It states the output is a JSON with a 'messages' array, which is useful. However, it does not disclose other behavioral traits such as idempotency, error handling for missing prompts, or performance characteristics. The name 'get' implies read-only, but it's not explicitly stated.

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 three sentences long, front-loading the purpose and then efficiently covering the return format and argument semantics. Every sentence serves a clear purpose without redundancy.

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 the presence of an output schema, the description does not need to detail the return structure further. It covers the essential aspects: purpose, output format, and argument format. It could mention what happens if the prompt is not found, but that may be handled by the output schema. Overall, it is fairly complete for a simple retrieval tool.

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 already covers both parameters with descriptions. The description adds value by clarifying that 'arguments' should be a dict mapping argument names to values, which is not explicit in the schema. This helps the agent understand the expected format of the object parameter.

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 'Get a prompt by name', which is a specific verb+resource. It distinguishes itself from the sibling 'list_prompts' which lists all prompts, making it clear this retrieves a single prompt. The optional arguments are also mentioned.

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 explains that arguments should be a dict mapping names to values, but does not provide guidance on when to use this tool versus alternatives like 'list_prompts'. It implies usage through the input format but lacks explicit when-to-use or when-not-to-use advice.

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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