VarynForge

Register derived asset

register_derived_asset

Register a derived asset (LinkedIn carousel PDF, social post, video, image) produced from an article suggestion. Appends a distribution-ledger row so the suggestion shows everything it produced — the article plus its derivatives — for content-ROI reporting (get_article_suggestion returns them as derivedAssets). Pass channel (reels | youtube | x | linkedin) so the app can show per-channel distribution status; register again with a new URL for repeat posts on the same channel — every registration is kept. scheduledFor records a future post date from an external scheduler (Buffer etc.) for display only — VarynForge never posts on your behalf. Derivative rows never affect publish status or Search Console attribution; use mark_article_published for the article itself.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes
`kind`	Yes
`channel`	No
`scheduledFor`	No
`suggestionId`	Yes

TDQS

A5/5.0

Behavior5/5

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

The description explicitly states side effects: it appends a row (additive, non-destructive), never affects publish status or Search Console attribution, and scheduledFor is for display only. This aligns with the annotations (readOnlyHint false, destructiveHint false) and adds useful context about non-impact.

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?

The description is dense but not wasteful; three sentences cover purpose, usage nuances, and parameter semantics without redundancy. Key constraints are front-loaded, making it easy to digest.

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?

Given the complexity of 5 parameters and 2 enums, the description covers all meaningful aspects: what it does, when to use, side effects, and parameter meanings. It also clarifies relationships with sibling tools, so an agent has full context. No output schema exists, so no return-value explanation is required.

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

Parameters5/5

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

With 0% schema description coverage, the description compensates fully. It explains each parameter: suggestionId (the source suggestion), kind (via examples like carousel PDF, social post), url (the asset link), channel (with allowed values), and scheduledFor (future post date for display only). No parameter is left undefined.

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 clearly states the tool's purpose: registering a derived asset (social post, video, etc.) from an article suggestion and appending a distribution-ledger row. It distinguishes itself from related tools like mark_article_published (which handles article publication status) and get_article_suggestion (which returns the derivatives).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance: use for any derived asset from an article suggestion, and clarifies that repeat posts can be registered again with a new URL. It also contrasts with mark_article_published, saying to use that for the article itself, leaving no ambiguity.

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 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.

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