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VarynForge

Get writer system prompt

get_writer_system_prompt
Read-only

Get the writer system prompt for drafting one content type from its brief. The writer works with your Varyn account context: every channel prompt binds to the brief, its acceptance rubric, and the opportunity behind the brief fetched from the account, and degrades to generic writing advice without them. Pass channel (article | x | linkedin | reels | youtube) — each channel has its own methodology: article covers voice adaptation, structure rules, and the acceptance checklist; x covers thread mechanics; linkedin covers post + carousel slides; reels and youtube cover the production script (brief -> script -> video). Load the prompt for the channel you are about to draft; article is the default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelYesarticle

TDQS

A4.8/5.0
Behavior5/5

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

The description reveals important behavioral details beyond the readOnlyHint annotation: the prompt binds to the brief, acceptance rubric, and opportunity context fetched from the account, and 'degrades to generic writing advice without them.' It also discloses per-channel methodology, giving an agent a realistic model of what the prompt will contain.

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 front-loaded with purpose, then behavioral context, then parameter guidance. It is dense but every sentence adds necessary information: what the tool does, how it behaves contextually, which channel values exist, and what each produces. There is no filler or redundancy.

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

Completeness5/5

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

With one enum parameter, a readOnly annotation, and no output schema, the description provides everything needed to call the tool correctly: the exact channel values, their meaning, the default, the expected behavior, and the fallback behavior. Returning a prompt is self-evident, so no return-format explanation is necessary.

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?

Schema description coverage is 0%, so the description carries the full burden for the `channel` parameter. It lists all enum values, explains what each channel covers, and notes the default. This fully compensates for the schema's lack of parameter descriptions.

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 states a specific action ('Get the writer system prompt') and resource ('for drafting one content type from its brief'), and further differentiates the tool from siblings by spelling out that each channel has its own methodology. This is far more specific than the title alone and leaves no ambiguity about what the tool returns.

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?

It explicitly instructs when to use the tool: 'Load the prompt for the channel you are about to draft' and states that article is the default. It also explains what each channel is for ('article covers voice adaptation... reels and youtube cover production script'). It does not name alternatives or explicitly say when not to use it, but the context is clear.

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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TDQS

A3.8/5.0
Disambiguation4/5

Most tools have distinct purposes, but a few pairs could confuse an agent: add_article_suggestion vs create_article_suggestion_with_input, and get_article_brief vs download_brief_markdown vs get_write_handoff all deal with brief content. The detailed descriptions help disambiguate, but the overlap is real.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in lowercase snake_case (create_project, list_opportunities, generate_article_brief, lint_draft). There is no mixing of camelCase, acronyms, or vague verbs, making the naming predictable and readable.

Tool Count2/5

50 tools is excessive for an MCP server, even for a broad platform like content operations. While the scope is large, this many tools will overwhelm agents and increase latency and context cost. Most practical servers are well under 25.

Completeness4/5

The tool surface covers the full content lifecycle: project creation, research, opportunity clustering, content planning, briefs, drafting, linting, publishing, and reporting. Minor gaps exist (e.g., no delete_project, no remove_destination, no direct analytics beyond distributions), but they are workarounds or handled in the web UI.

Resources