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Fetch a chosen Looba snippet and receive integration instructions adapted to your project's framework, CSS approach, and naming conventions.

Instructions

Fetch a Looba snippet and return it with detailed integration instructions tailored to the user's project. The AI assistant MUST use the project_context to adapt class names, CSS variables, imports, and structure to match the target codebase. REQUIRES a valid proposal_token from a recent propose_snippets call and a user_choice matching the slug — the server enforces the 'propose 3, user picks one' workflow.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesThe slug of the option the user picked (must match user_choice in the proposal_token)
user_choiceYesWhich option the user picked: 1, 2, or 3 (from the 3 options returned by propose_snippets)
proposal_tokenYesThe proposal_token returned by the most recent propose_snippets call. Required.
project_contextYesDescription of the target project: framework (React/Vue/Svelte/vanilla/Next.js...), CSS approach (CSS modules, Tailwind, styled-components, SCSS, global CSS...), naming conventions (BEM, camelCase...), existing CSS variables or design tokens, component patterns, and the target file path where the snippet will be placed.
target_fileNoThe file path where the snippet will be integrated (helps with import paths)
Behavior4/5

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

Discloses the adaptive behavior (using project_context to tailor output) and the required preconditions. Without annotations, it covers key behaviors but could mention idempotency or side effects.

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?

Two sentences: first states purpose, second details workflow requirement. No redundant words, front-loaded with key information.

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?

Completeness is high given no output schema; explains input and workflow but lacks detail on return format or error conditions. Still sufficient for understanding context.

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?

Schema descriptions cover all parameters, but the description adds workflow context (e.g., 'project_context' adapts class names, 'target_file' aids imports). This enhances beyond baseline 3.

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 it fetches a Looba snippet and returns tailored integration instructions. It distinguishes from siblings like propose_snippets by specifying the prerequisite workflow.

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?

Explicitly requires a valid proposal_token and user_choice from propose_snippets, enforcing the 'propose 3, user picks one' workflow. This makes it clear when this tool should be used relative to siblings.

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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