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get_prompt

Retrieve pre-configured prompts by name with customizable arguments for Pine Script v6 development, returning structured JSON with messages for AI-assisted coding tasks.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context: it describes the return format ('rendered prompt as JSON with a messages array') and specifies that arguments are optional and provided as a dict. However, it lacks details on error handling, authentication needs, rate limits, or side effects. For a tool with no annotations, this is a moderate but incomplete effort.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two sentences that are front-loaded: the first states the core purpose, and the second adds key behavioral details. There's no wasted text, but it could be slightly more structured (e.g., separating usage notes). Overall, it's efficient and earns its place.

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 tool's moderate complexity, 100% schema coverage, and the presence of an output schema (implied by 'Returns the rendered prompt as JSON'), the description is reasonably complete. It covers the purpose, return format, and parameter usage, though it lacks guidance on when to use versus siblings. With annotations absent, it could benefit from more behavioral context, but the output schema reduces the need to explain return values.

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%, so the schema already documents both parameters ('name' and 'arguments') fully. The description adds minimal value beyond the schema: it reiterates that arguments are optional and should be a dict mapping, but doesn't provide additional syntax, examples, or constraints. This meets the baseline of 3 for high schema coverage.

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 the tool's purpose: 'Get a prompt by name with optional arguments.' This specifies the verb ('Get'), resource ('prompt'), and key parameters. It distinguishes from siblings like 'list_prompts' (which lists rather than retrieves specific prompts) and 'get_doc' (which retrieves documents, not prompts). However, it doesn't explicitly contrast with all siblings, keeping it at 4 rather than 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose 'get_prompt' over 'list_prompts' for browsing, 'get_doc' for document retrieval, or other siblings. There's no context about prerequisites, typical use cases, or exclusions, leaving the agent with minimal usage direction.

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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