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Glama

hooklayer

by io.github.khan-ashifur

score_hook

Idempotent

Score a TikTok, Reels, or Shorts hook against proven viral patterns. Returns a 0-100 score, percentile rank, matched pattern, strengths, weaknesses, and three improved hook variations. Use when the user has a draft hook to validate, wants to compare alternatives, or needs feedback before publishing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe hook to score (3-200 chars typical)
nicheNoOptional niche context (e.g. "Beauty & Skincare", "Finance & Business")
platformYesTarget platform — affects pattern matching

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
whyNoOne-sentence verdict explaining the score
scoreNoHook quality score (0-100)
rewritesNoThree rewritten versions at higher quality
breakdownNoSub-scores (0-100) for five quality dimensions
from_paygNoWhether credits came from pay-as-you-go balance
strengthsNoWhat the hook does well
percentileNoPercentile rank vs all scored hooks
weaknessesNoWhat could be improved
pattern_matchNoMatched viral pattern name
credits_remainingNoCredits remaining after this call
from_subscriptionNoWhether credits came from subscription

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "breakdown": {
+      "description": "Sub-scores (0-100) for five quality dimensions",
+      "properties": {
+        "curiosity": {
+          "type": "number"
+        },
+        "emotional_pull": {
+          "type": "number"
+        },
+        "pattern_alignment": {
+          "type": "number"
+        },
+        "specificity": {
+          "type": "number"
+        },
+        "urgency": {
+          "type": "number"
+        }
+      },
+      "type": "object"
+    },
+    "credits_remaining": {
+      "description": "Credits remaining after this call",
+      "type": "number"
+    },
+    "from_payg": {
+      "description": "Whether credits came from pay-as-you-go balance",
+      "type": "boolean"
+    },
+    "from_subscription": {
+      "description": "Whether credits came from subscription",
+      "type": "boolean"
+    },
+    "pattern_match": {
+      "description": "Matched viral pattern name",
+      "type": "string"
+    },
+    "percentile": {
+      "description": "Percentile rank vs all scored hooks",
+      "type": "number"
+    },
+    "rewrites": {
+      "description": "Three rewritten versions at higher quality",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "score": {
+      "description": "Hook quality score (0-100)",
+      "type": "number"
+    },
+    "strengths": {
+      "description": "What the hook does well",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "weaknesses": {
+      "description": "What could be improved",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "why": {
+      "description": "One-sentence verdict explaining the score",
+      "type": "string"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already cover idempotency and non-destructiveness, so the description's job is lighter. It adds useful context about what the tool returns and that platform affects pattern matching, but it does not clarify whether any state changes occur or how much input content is processed. This is acceptable given the annotations, but still not rich.

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 compact and front-loaded, with a clear first sentence of what the tool does, followed by the result components and the explicit usage cases. Every sentence earns its place without redundant filler.

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 simple parameter count, full schema coverage, available annotations, and presence of an output schema, the description is complete enough for an agent to invoke this tool correctly. It covers the target content, the decision, and the expected output categories.

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?

Schema description coverage is 100%, so the schema already documents `text`, `niche`, and `platform`. The description adds no per-parameter semantics beyond a minor mention that the platform affects pattern matching, which is already implied in the schema. Baseline 3 is appropriate.

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 uses a specific verb ('Score') and identifies the exact resource ('a TikTok, Reels, or Shorts hook') plus the method ('against proven viral patterns'). It clearly distinguishes this from sibling tools by focusing specifically on hooks and their improved variations, rather than broader virality prediction or account analysis.

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 explicitly states when to use the tool: 'Use when the user has a draft hook to validate, wants to compare alternatives, or needs feedback before publishing.' It provides clear invocation context but does not name alternative tools or include explicit when-not-to-use guidance, keeping it one step below full differentiation.

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

A4.1/5.0
Disambiguation4/5

Each tool targets a distinct purpose: analysis, trend research, scoring, creation, and tracking. The lifecycle tools (analyze_account, watch_account, get_changes) are related but clearly delineated by baseline creation vs. comparison. score_hook and predict_virality could be mildly confused since both score content, though descriptions clarify hook-level vs. full-draft scope.

Naming Consistency4/5

Nine of twelve tools follow the verb_noun pattern (analyze_account, find_viral_template, get_changes, list_watches, match_voice, predict_virality, score_hook, search_videos, watch_account). Three deviate: brief_to_blueprint, trend_pulse, and viral_remix use prepositional or noun-based naming, creating minor inconsistency but not confusion.

Tool Count5/5

Twelve tools for a short-form content intelligence platform feels well-scoped. The surface covers research (4 tools), creation (3), validation (2), and tracking (3), with each tool earning its place. This sits comfortably within the ideal 3-15 range for a domain-specific server.

Completeness3/5

The research, creation, and validation workflows are well-covered, but the tracking lifecycle has a notable gap: watch_account and list_watches exist with no unwatch/remove_watch tool, and get_changes implies persistence without a way to manage it. Also missing is a single-video deep-dive tool (analyze_account only covers videos within full account analysis) and direct creator-to-creator comparison.

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