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VarynForge

by com.varynforge

Create content plan from opportunities

create_content_plan_from_opportunities

Create a content plan by harvesting the top-30 opportunity clusters from a completed research run. Auto-creates article suggestions linked to each cluster. Left out of the harvest: dismissed clusters (set_opportunity_status), keywords matching the project exclusion terms (set_excluded_terms), and clusters the site already covers (>=80% of keywords covered — refresh work on existing pages surfaces via editorial scores, not here).

Input Schema

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

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

TDQS

A4.4/5.0
Behavior5/5

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

Discloses meaningful side-effect behavior beyond the annotations: it auto-creates article suggestions, harvests a limited set of top-30 clusters, and applies three explicit exclusion rules. This matches the annotations (readOnlyHint=false, destructiveHint=false) and adds substantial context about what the tool will and will not touch.

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 sentences, front-loaded with the core action, followed by linked side effects and exclusion rules. Every sentence earns its place with no filler or repetition.

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?

Covers prerequisites, scope, side effects, and exclusions thoroughly for a two-parameter tool. It does not state what the tool returns (e.g., IDs of created article suggestions), and there is no output schema, which is a minor gap.

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

Parameters3/5

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

The description does not directly explain either parameter, and schema coverage is only 50%. However, projectId is reasonably inferable as the project with a completed research run, and the context parameter is well documented in the schema. Acceptable but the description adds no parameter-level value.

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?

States a specific verb and resource: creates a content plan by harvesting top-30 opportunity clusters from a completed research run. It also distinguishes itself from single-suggestion creation by noting it auto-creates article suggestions linked to each cluster.

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?

Implies when to use it (after a completed research run) and explicitly states what is left out: dismissed clusters, excluded terms, and already-covered clusters. It even names related siblings like set_opportunity_status and set_excluded_terms, though it does not fully enumerate alternative tools for all situations.

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