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AINative-Studio

AINative PRD Generator MCP Server

Official

prd_generate

Generates comprehensive PRDs for AINative features using AI with full platform context. Auto-saves to ZeroMemory for cross-session recall.

Instructions

Generate a comprehensive Product Requirements Document (PRD) for an AINative feature, integration, or product. Uses AI with full AINative platform context — knows all services, APIs, SDKs, and architectural constraints. The PRD is automatically saved to ZeroMemory for cross-session recall.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
templateNoPRD template to use (default: standard)standard
constraintsNoTechnical or business constraints
descriptionYesDetailed description of what needs to be built
issue_numberNoOptional GitHub issue number to link to
product_nameYesName of the product or feature
core_featuresYesList of core features to include
target_audienceYesWho will use this product/feature
ainative_servicesNoAINative services this feature will use (e.g., ["ZeroDB", "ZeroMemory", "Agent Cloud"]). If omitted, the generator will auto-detect relevant services.
additional_contextNoAny additional context, links, or requirements
Behavior2/5

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

No annotations are provided, so the description bears full burden. It mentions auto-saving to ZeroMemory and AI context, but omits details like idempotency, overwrite behavior, permissions, cost, or whether generation is synchronous. Significant gaps remain.

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 two sentences, front-loaded with purpose, and every sentence adds value. No redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 9 parameters, no output schema, and no annotations, the description is too sparse. It does not explain return format, error handling, or how to verify completion. Missing critical context for a complex generation 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?

Schema coverage is 100%, so baseline is 3. The description adds value by noting auto-detection for ainative_services when omitted, which goes beyond the schema. Minor extra context earns a score of 4.

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 the tool generates a comprehensive PRD for AINative features, specifying the verb and resource. It distinguishes from siblings by highlighting AI platform context and automatic ZeroMemory saving.

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 use when a full PRD is needed for AINative products, but lacks explicit when-not-to-use or alternative tool references. Siblings like prd_from_issue or prd_refine are not mentioned, so guidance is implicit rather than explicit.

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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