VarynForge

Create article suggestion with input

create_article_suggestion_with_input

Create a brief-ready article suggestion from the operator's own input. Auto-resolves a default content plan; creates/links search-query rows for the keywords; returns suggestionId + contentPlanId. Free-tier compatible (no research run required).

Input Schema

TableJSON Schema

Name	Required	Description
`angle`	No
`title`	Yes
`context`	Yes	One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.
`projectId`	Yes
`reasoning`	No
`searchIntent`	No
`primaryKeyword`	Yes
`targetAudience`	No
`wordCountTarget`	No
`secondaryKeywords`	No

Schema Changelog

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

Changed2 schema fields changedSep 4, 2026

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: -[
-  "projectId",
-  "title",
-  "primaryKeyword"
-]New value: +[
+  "projectId",
+  "title",
+  "primaryKeyword",
+  "context"
+]

First observedAug 3, 2026

TDQS

A4/5.0

Behavior4/5

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

With only readOnlyHint=false and destructiveHint=false in annotations, the description carries the burden and discloses key side effects: auto-resolving a default content plan, creating/linking search-query rows for keywords, and returning suggestionId + contentPlanId. It does not however disclose failure behavior, idempotency, or duplicate-creation behavior, so it is not a 5.

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?

Three tight sentences lead with the main action, then give side effects/return values and the free-tier condition. No filler or repetition; every sentence earns its place.

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

Completeness3/5

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

For a 10-parameter tool with no output schema, the description gives the core action, side effects, and return IDs, but it does not cover the semantics of the optional parameters or the default-content-plan behavior. It is sufficient for a basic call, but not fully complete for advanced usage.

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

Parameters2/5

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

Schema description coverage is only 10% (only the context parameter is described), and the tool description does little to explain the other nine parameters. It references 'keywords' broadly and 'operator's own input,' but angle, reasoning, searchIntent, targetAudience, wordCountTarget, and secondaryKeywords remain semantically unexplained.

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 opens with a specific verb and object: 'Create a brief-ready article suggestion from the operator's own input.' It further differentiates the tool by noting that it auto-resolves a default content plan, links search-query rows, and requires no research run, which helps distinguish it from siblings like start_research_run or create_content_plan_from_opportunities.

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 clearly indicates when to use this tool: when the operator has their own input and wants a brief-ready suggestion on the free tier ('no research run required'). It does not explicitly name alternatives or state when not to use it, but the context is clear enough for routing.

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

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