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VanguardiaAI

motionprompts MCP

by VanguardiaAI

Get a component prompt + motion tokens (no rendering)

get_component_prompt

Retrieve a component build prompt, its motion system tokens, and an instruction to adapt the prompt to those tokens yourself, preserving arithmetic relationships for consistent code.

Instructions

THE way to get a build prompt. Returns three blocks: (a) the standalone prompt VERBATIM, untouched; (b) the tokens of the requested motion system; (c) an explicit instruction telling you to adapt (a) to (b) YOURSELF, preserving arithmetic relationships — if a duration holds up a sum, a chained sequence or a ScrollTrigger end, recompute the whole set, never a lone value. Replaces render_prompt, which substituted mechanically and produced prompts whose prose contradicted their code (11/30 in judged evaluation). See DECISIONS.md D15 and D18.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYes
motion_systemNoDefault: the component's native system.
Behavior5/5

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

With no annotations provided, the description carries full burden. It discloses the exact output structure (three blocks), the fact that it returns an instruction to adapt the prompt manually, and specifies the arithmetic preservation rule (recompute chain/sum/ScrollTrigger end). It also states what the replaced tool did wrong, offering deep behavioral context.

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 dense but well-organized, with each sentence contributing a distinct purpose (proclamation, output structure, replacement rationale, reference). It is longer than ideal but earns its length through the critical arithmetic detail and forward references.

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?

The description covers the tool's purpose, output composition, usage recommendation, and subtle calculation instruction, which is robust for a tool with no output schema. It lacks an explicit enumeration of return format fields or error conditions, but the provided information is sufficiently complete for selection and basic invocation.

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?

The description adds some context for motion_system via 'requested motion system', but the required parameter 'slug' is completely unexplained in both the schema and description. Since schema coverage is only 50%, the description should compensate, but it does not address the required parameter at all, leaving a significant gap.

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 uses a specific verb+resource ('get a build prompt') and clearly states it returns three blocks, defining the tool's scope. It explicitly distinguishes itself from sibling render_prompt by name and by explaining the difference, making the purpose unambiguous.

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

Usage Guidelines5/5

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

The description proclaims 'THE way to get a build prompt' and explicitly states it 'Replaces render_prompt', offering a direct when/why to use this tool over a named alternative. It even provides a rationale (render_prompt substituted mechanically and produced contradictory prompts), giving clear usage guidance.

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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