Skip to main content
Glama

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
suggestionIdYes

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only mark readOnlyHint=true, but the description adds substantial behavioral detail: which fields every brief carries, which fields are article-specific, the effect of compact, and the exact hasBrief/briefStatus/queuedUntil shape returned when a brief does not exist. It also explains the meaning of 'queued' and the daily-cap reset timing. This goes far beyond the structured annotations and is internally consistent.

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?

Every sentence earns its place. The purpose is front-loaded first, then channel selection, then the payload fields, then the missing-brief status behavior. The description is information-dense but well-ordered, and none of it is redundant with the schema or annotations.

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 no output schema, the description correctly takes on the burden of explaining return values: hookStatement, keyStats structure, always-populated fields, and the full missing-brief response. It also covers the unusual queue-forging state, which an agent would otherwise be unable to interpret. The description is complete for a retrieval tool with this parameter set.

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 0%, so the description must compensate, and it does for channel and compact: channel values are enumerated with 'article default' clarified and compact's effect on referenceArticles is stated. suggestionId is not explicitly described, but its role is clear from the opening sentence and the schema's UUID format. This is strong compensation, though not exhaustive for every parameter.

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 resource: 'Get the full content brief for an article suggestion.' It clearly distinguishes this from siblings like generate_article_brief by framing itself as retrieval of an existing brief and detailing per-channel behavior. The description makes the tool's scope obvious without requiring the schema.

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 gives clear context on how to use the tool: pass channel to select a channel brief, pass compact=true to drop referenceArticles, and poll after queuedUntil if the brief isn't ready. It does not explicitly name alternatives or exclusion cases, such as 'use generate_article_brief to create a missing brief,' so it falls short of a 5. Overall, an agent knows when and how to call this tool correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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