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Buzzword Density

by io.github.pipeworx-io

Buzzword Density Analyze

buzzword_density_analyze
Read-onlyIdempotent

Analyze a text passage for overused industry jargon using a tech, finance, consulting, startup, or combined dictionary. Returns buzzword count, density score (0-100), severity level, flagged terms, and an optional roast critique when roast=true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roastNoGet a roast of your writing
contentYesText to analyze
industryNoIndustry dictionary to use

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
roastNoHumorous critique of the writing (when roast_mode=true)
density_scoreYesBuzzword density score on a scale of 0-100
flagged_termsYesList of identified buzzwords and their frequency
buzzword_countYesTotal number of buzzwords found in the text
severity_levelYesSeverity classification of buzzword usage

Schema Changelog

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

  1. Added
  2. Removed
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations indicate read-only, idempotent, open-world, non-destructive. Description adds details on return values (count, score, severity, flagged terms) and optional roast, enhancing transparency without contradiction.

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?

Single sentence with all key elements: action, dictionaries, output, optional feature. No redundant words.

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 output schema presence, description covers tool purpose, parameters, and return information sufficiently for a simple analysis tool.

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

Parameters4/5

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

Schema coverage is 100%. Description adds meaning by listing output metrics and explaining roast parameter effect, surpassing baseline of 3.

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?

Description clearly states the tool analyzes text for overused industry jargon using specific dictionaries, and lists return metrics. Distinct from sibling tools which are unrelated.

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?

Description implies use for analyzing buzzwords in text, with optional roast. No explicit when-not-to-use, but sibling tools are different domains, so context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Buzzword Density toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple ask_pipeworx variants and several prediction market tools. This causes ambiguity for agents trying to select the right tool.

Naming Consistency2/5

Tool names mix verb_noun patterns (ask_pipeworx, forget) with noun phrases (entity_profile) and inconsistent prefixes (pipeworx_, polymarket_). No consistent naming convention.

Tool Count2/5

With 32 tools, the set is excessive for a server named 'Buzzword Density' and includes many redundant or overlapping tools. A more focused set of 10-15 would be more coherent.

Completeness4/5

The tool set covers a wide range of data sources and operations (retrieval, comparison, monitoring, memory), missing only minor lifecycle operations like updating stored data.

Resources