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Glama

VarynForge

by com.varynforge

List opportunities

list_opportunities
Read-only

List content opportunity clusters sorted by opportunity score. Goal filters: fast_wins, high_intent, authority_building, competitor_gap. coverage filters by site-coverage status and defaults to ["gap","partial"] — clusters the site already COVERS (ranks top-10 for a cluster keyword) are hidden unless "covered" is included (refresh candidates). Dismissed clusters (set_opportunity_status) are hidden, and keywords matching the project exclusion terms (set_excluded_terms) down-weight their cluster scores. Clusters flagged by the asset-fit assessment (fitFlag unservable_feature | off_catalog, with fitReason explaining why) are down-weighted rather than hidden — treat them as comparison-content candidates at best, never headline opportunities. Scores are normalized 0-100 against the highest-scoring visible cluster, so coverage filtering, dismissing, excluding, or fit-flagging can shift the relative numbers of other clusters.

Input Schema

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

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",
-  "limit"
-]New value: +[
+  "projectId",
+  "limit",
+  "context"
+]
  2. Changed1 schema field changed
    • addedInput schema / properties / coverage
      Added value: +{
+  "items": {
+    "enum": [
+      "gap",
+      "partial",
+      "covered"
+    ],
+    "type": "string"
+  },
+  "type": "array"
+}
  3. First observed

TDQS

A3.8/5.0
Behavior5/5

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

The description goes far beyond the readOnlyHint annotation by disclosing non-obvious behaviors: default coverage filtering to ['gap','partial'], hiding of dismissed clusters, down-weighting from excluded terms and fit flags, and score normalization that can shift relative numbers. This is exactly the behavioral context an agent needs to interpret results correctly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and each subsequent sentence contributes unique behavioral detail. It is dense and arguably could be more scannable with bullets, but it contains no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description explains the most important output interpretations — which clusters appear, what is hidden vs. down-weighted, and how scores normalize — but omits return-value structure and pagination behavior, and there is no output schema to fill that gap. The unexplained optional parameters further reduce completeness.

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 adds real meaning to `goal` and `coverage` by explaining default values, filter semantics, and hidden vs. down-weighted effects — valuable beyond the bare schema enums. However, schema description coverage is only 13%, and six other parameters such as cursor, activeMetaCluster, and excludeInContentPlan receive no explanation in the description.

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 'List content opportunity clusters sorted by opportunity score' — a specific verb, resource, and ordering. This unambiguously identifies what the tool does and implicitly distinguishes it from detail-oriented siblings like get_opportunity_detail.

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

Usage Guidelines2/5

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

No explicit when-to-use guidance or alternative routing is provided. The description references set_opportunity_status and set_excluded_terms only as state influences, not as alternatives, and never explains when to choose this over get_opportunity_detail or create_content_plan_from_opportunities.

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.

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