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Glama

VarynForge

by com.varynforge

Get article brief

get_article_brief
Read-only

Get the full content brief for an article suggestion. A suggestion holds one brief per channel — pass channel (article default | reels | youtube | x | linkedin) to pick which. Every brief carries hookStatement (one verbatim-usable hook sentence naming the reader payoff); article briefs also carry keyStats (a stat bank of { claim, value, source, sourceUrl } entries for evidence blocks and data slides). Pass compact=true to drop referenceArticles (informational only). internalLinks, curatedTargetQueries, and targetKeywords always populate. When the requested channel's brief does not exist yet, returns { hasBrief: false, briefStatus: 'queued' | 'forging' | 'none', queuedUntil } — 'queued' means the forge is waiting for the free daily cap to reset at queuedUntil; poll after that moment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelNo
compactNo
contextYesOne sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.
suggestionIdYes

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: -[
-  "suggestionId"
-]New value: +[
+  "suggestionId",
+  "context"
+]
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

With readOnlyHint already declared, the description adds substantial behavioral detail: the per-channel brief model, compact dropping referenceArticles, guaranteed populated fields, and the exact fallback object with hasBrief: false and statuses queued/forging/none plus queuedUntil. It even explains polling after queuedUntil. This goes well beyond the annotation baseline.

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?

Each sentence adds distinct information: purpose, channel selection, brief fields, compact behavior, guaranteed fields, and fallback semantics. The description is dense but front-loaded with the purpose and the most critical details. No filler or redundant repetition of schema 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?

Given no output schema, the description covers the main return expectations: hookStatement, keyStats, referenceArticles, and the fallback object. It doesn't enumerate every field of a full brief, but it provides enough for an agent to invoke and interpret the result correctly. The missing-brief scenario is explicitly described, which is important for handling edge cases.

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 description coverage is only 25% (only context is described), so the description carries the burden. It thoroughly defines channel values and default, and explains compact's effect on referenceArticles. SuggestionId is only implied as the suggestion identifier, but its meaning is inferable from the tool's purpose and the sibling get_article_suggestion.

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 'Get the full content brief for an article suggestion', stating a specific verb and resource. It differentiates from sibling tools like generate_article_brief by focusing on retrieval of an existing brief, and explains the per-channel structure. This is unambiguous and distinct.

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 clearly implies this is the read path for existing briefs, with explicit guidance on channel selection and the behavior when a brief doesn't exist. It does not name sibling tools like generate_article_brief as alternatives, but the context is clear enough for an agent to decide when to call it. No exclusions are given, but the usage context is well established.

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