VarynForge

Accept idea

accept_idea

Commit an expanded idea (from expand_idea) to the content plan as a brief-ready article suggestion. Stores the verbatim idea as provenance, lands the suggestion in generating_brief, and forges its brief automatically (free tier: queues past the daily cap). Returns the new suggestionId.

Input Schema

TableJSON Schema

Name	Required	Description
`angle`	Yes
`title`	Yes
`context`	Yes	One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.
`ideaText`	Yes
`nicheFit`	No
`projectId`	Yes
`reasoning`	No
`searchIntent`	Yes
`demandEstimate`	No
`relatedQueries`	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",
-  "ideaText",
-  "title",
-  "angle",
-  "searchIntent"
-]New value: +[
+  "projectId",
+  "ideaText",
+  "title",
+  "angle",
+  "searchIntent",
+  "context"
+]

First observedAug 3, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Beyond the minimal readOnly/destructive annotations, the description discloses concrete state changes: verbatim provenance storage, placement into generating_brief, automatic brief creation, free-tier daily-cap queueing, and the return of a suggestionId. This gives the agent a realistic model of side effects and edge behavior.

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?

Two sentences with the main action front-loaded and the consequential details packed compactly. There is no filler, no repetition of schema metadata, and every clause adds meaningful information.

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?

The description is complete about workflow state, return value, and free-tier behavior, and explicitly stating the suggestionId return is important because no output schema exists. It is less complete on input-field semantics, but the upstream expand_idea relationship mitigates that for the intended pipeline 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%, yet the description does not explain required parameters such as angle, searchIntent, title, or context. It only implicitly references ideaText through 'expanded idea', leaving six required fields poorly documented from the agent's perspective.

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 first sentence names the exact action ('Commit'), the input ('an expanded idea'), and the concrete outcome ('brief-ready article suggestion' in the content plan). It explicitly ties the tool to expand_idea, which clearly distinguishes it from the many sibling suggestion-related tools.

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?

The description tells the agent this is the intended follow-up to expand_idea, so the workflow position is clear. It does not explicitly state when to prefer alternatives like add_article_suggestion or create_article_suggestion_with_input, but the context is strong enough for most routing decisions.

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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