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VarynForge

by com.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
contextYesOne sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "channel"
-]New value: +[
+  "channel",
+  "context"
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations provide readOnlyHint=true, and the description adds meaningful behavioral context: the prompt binds to account context and degrades to generic writing advice without it. It also discloses channel-specific methodology differences, which goes beyond the structured annotation.

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 purposeful, front-loading the tool's purpose then expanding into channel-specific details. Every sentence adds information, though slightly more length than strictly necessary for the simplest reading.

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?

Given no output schema, the description gives enough about what the prompt contains and how it varies by channel. It covers the key operational nuance (degradation without account context) and leaves little ambiguity for an agent deciding to call this 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 only 50%, with context described but channel lacking a description beyond enum/default. The description compensates strongly for channel by explaining what each enum value covers and confirming the default, while the context parameter is already adequately described in the schema.

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?

States a specific verb and resource ('Get the writer system prompt for drafting one content type from its brief'), and immediately differentiates channel-specific behaviors. It clearly identifies what the tool returns and how it relates to the broader drafting workflow.

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 instructs when to use it ('Load the prompt for the channel you are about to draft') and notes the default channel. It does not name sibling alternatives like get_write_handoff or get_instructions, but the channel-specific guidance makes usage context reasonably 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 target a distinct resource and action, and the descriptions carefully cross-reference close alternatives (e.g., add_article_suggestion vs create_article_suggestion_with_input vs accept_idea). A few brief/read variants like get_article_brief, get_write_handoff, and download_brief_markdown could still be confused despite helpful explanations, so the set is not perfectly unambiguous.

Naming Consistency5/5

Tool names follow a highly consistent verb_noun snake_case pattern throughout: get_*, list_*, create_*, update_*, set_*, add_*, delete_*, start_*, expand_*, etc. Even the less common names like lint_draft and remap_asset are still clear verb_noun constructions.

Tool Count1/5

At 57 tools, this far exceeds the 50+ extreme threshold for a single MCP server. Even for a broad content workflow, the surface is overwhelming and would benefit from consolidation or splitting into focused servers.

Completeness4/5

The tool set covers the full content lifecycle: project setup, research runs, opportunity clustering, suggestion creation, brief generation, drafting, linting, publishing, reporting, and account management. Minor gaps exist—destinations and projects cannot be deleted via MCP, and there is no direct update for article suggestion metadata—but these are workable via the web UI or existing tools.

Resources