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Render PRD

render_prd

Render a project spec into a Draftlytic-style Markdown PRD, including features grouped by priority, acceptance criteria checklists, and data model tables.

Instructions

Render a project spec into a deterministic, Draftlytic-style Markdown PRD. A project spec: name, overview, target_audience, platforms[], tech_stack[], features[] ({title, description, priority: must-have|nice-to-have|future, acceptance_criteria?[]}), screens[]? ({name, purpose}), data_model[]? ({entity, fields[]: {name, type, notes?}}), constraints[]?, non_goals[]?, revenue_model?. Output includes: title, overview, target audience, platforms, tech stack, features grouped by priority (must-have / nice-to-have / future) with acceptance-criteria checklists, screens & navigation, data model tables, constraints, and non-goals. Run validate_spec first — this tool renders whatever it's given, even an incomplete spec.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
specYesThe spec object to render, as a JSON value (not a JSON string).
Behavior4/5

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

No annotations provided, but description details the output structure comprehensively: sections like title, overview, features grouped by priority, data model tables, etc. States determinism and Draftlytic-style. Does not mention side effects or authentication, but for a render tool with no side effects, this is adequate.

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?

Description is approximately 5 sentences, each providing essential information. Front-loaded with the primary action ('Render a project spec...'), followed by input format, output details, and a usage note. No redundancy or unnecessary words.

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?

For a 1-parameter tool with no output schema and no annotations, the description is thorough. It explains the input structure, output format, and a prerequisite. A minor gap: it does not explicitly state that the output is a Markdown string (though implied by 'Markdown PRD'), but overall it is quite complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with a single 'spec' parameter described as a JSON value. Description adds substantial meaning by enumerating the expected fields of the spec (name, overview, target_audience, platforms, tech_stack, features with detailed subfields, screens, data_model, constraints, etc.). This goes far beyond the schema's minimal description.

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?

Description clearly states the tool's purpose: rendering a project spec into a deterministic Draftlytic-style Markdown PRD. It specifies input (project spec with detailed fields) and output (structured PRD with sections). Differentiates from siblings (validate_spec, spec_checklist, open_in_draftlytic) by focusing on rendering.

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

Usage Guidelines4/5

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

Explicitly advises to run validate_spec first, which guides the agent on proper sequencing. Also notes that the tool accepts incomplete specs, setting expectations. Does not fully specify when 'not' to use it, but provides clear context for usage.

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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