word_frequency
Count word frequencies in text for content/marketing analysis. Returns: {words: [{word, count, pct}], total}
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| top_n | No |
Marketing Mcp
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- Added
TDQS
B3.4/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description should disclose behavioral traits; it only provides a return format but omits details like stop word handling, case sensitivity, or punctuation treatment.
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 concise: two sentences that front-load purpose and return format without unnecessary detail.
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?
Given 2 parameters with no output schema or annotations, the description is partially complete but lacks details on parameter semantics and behavioral nuances.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description should explain parameters; it does not clarify 'text' or 'top_n' beyond what the schema provides (type and default).
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 counts word frequencies for content/marketing analysis, with a distinct focus from sibling tools like analyze_seo_keywords.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for content/marketing analysis but does not specify when to use this tool versus alternatives, nor when not to use it.
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
C2.9/5.0
Disambiguation3/5
The tools analyze_seo_keywords and word_frequency both perform word frequency analysis, causing potential confusion. The health_check tool is unrelated to marketing, and extract_meta_tags is distinct.
Naming Consistency3/5
Tool names mix verb-initial (analyze_seo_keywords, extract_meta_tags) and noun-initial (health_check, word_frequency) patterns, with inconsistent use of verbs across names.
Tool Count4/5
With 4 tools, the server is slightly thin for a marketing assistant but still reasonable given its focus on basic analysis.
Completeness3/5
The server covers basic SEO keyword and meta tag analysis but lacks tools for content generation, optimization, or competitor analysis, leaving notable gaps.