Fluff
Server Details
Find filler and buzzwords in text and score how much of it says nothing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-11-25
- URL
TDQS
Scored across 1 tool
There is only one tool, so there is no possibility of misselection or overlapping purpose. The tool's trigger conditions (tighten/shorten/cut fluff, or measure filler) are stated explicitly, leaving no boundary confusion.
The single name `fluff_check_filler` follows a clear server-prefixed verb/object pattern in snake_case. With one tool, consistency is trivially satisfied and the name is self-describing.
One tool is thin against the typical 3-15 range, and the surface is a single analysis call. It is at least well matched to a deliberately narrow purpose, so it is borderline rather than a severe mismatch.
For a fluff-detection domain the tool covers the core need: a percentage, verdict, per-sentence reasons and recurring filler phrases, and it is deterministic. Minor gaps exist (no batch input, no configurable strictness, no rewrite output), but the description explicitly hands rewriting back to the caller, so these are workable.
Available Tools
1 toolfluff_check_fillerFind the sentences that say nothingARead-onlyIdempotentInspect
Use when the user asks to tighten, shorten or cut the fluff from an email, memo, post, report or essay, or asks how much of a text is filler. Flags sentences that are pleasantries, buzzwords, throat-clearing, repeats or vague claims with no facts, names or numbers, and returns a fluff percentage, a verdict, the flagged sentences with why, and the filler phrases that recur. Deterministic, so the same text always gives the same result. Example: {"text": ""}. Rewrite the flagged parts yourself.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to check (up to 60,000 characters). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), yet the description still adds real value: it promises determinism ('same text always gives the same result') and explicitly states the tool does not rewrite. The missing piece is any note on cost or handling of the 60,000-character ceiling, which lives only in the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences plus an example, front-loaded with the trigger condition, then the detection criteria, then the return shape. Every clause carries information an agent needs; nothing is repeated from annotations or the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, and the description compensates by naming all four return components. For a single-parameter, read-only analysis tool, there is nothing further an agent needs in order to invoke it correctly and interpret the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With one required parameter at 100% schema coverage, the baseline is 3. The description goes slightly beyond by supplying a concrete invocation example mapping the parameter to its intent ('<the draft to tighten>'), which clarifies that the input is a draft, not a finished artifact.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('flags sentences that are pleasantries, buzzwords, throat-clearing...'), and enumerates the exact outputs (fluff percentage, verdict, flagged sentences with why, recurring filler phrases). An agent knows precisely what it gets back without opening any schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Opens with an explicit trigger: 'Use when the user asks to tighten, shorten or cut the fluff... or asks how much of a text is filler.' It also draws the responsibility boundary with 'Rewrite the flagged parts yourself,' telling the agent what this tool will not do. No siblings exist, so no alternative routing is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
- Added
fluff_check_filler - Removed
fluff_check_text - Removed
fluff_gist_text
2 tool updates
- First observed
fluff_check_text - First observed
fluff_gist_text
Related MCP Connectors
Scores text for substance, depth, and clarity in under 25ms.
Score text 0 to 100 for chatbot habits like filler, em dashes and buzzwords, each one marked.
11Prose linter + AI-slop detector: weasel words, passive voice, hedging, and research-cited AI tells
English readability scores (Flesch, Flesch-Kincaid, Fog, SMOG) and text stats. Text never stored.
Related MCP Servers
- AlicenseAqualityDmaintenanceDeterministic fluff detector for AI-generated prose. No model, no API key.32MIT
- AlicenseNot gradedqualityBmaintenanceAnalyzes text for overused industry jargon, returning buzzword count, density score, severity level, and flagged terms. Optionally provides humorous critique in roast mode.230 npmMIT
- AlicenseAqualityAmaintenanceFlag AI tells, weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, and filler adverbs49MIT
- FlicenseNot gradedqualityAmaintenanceAnalyzes text and files for formulaic AI writing patterns, returning a 0-100 score, band label, specific violations with context, and concrete revision advice. Runs entirely locally with rule-based heuristics, requiring no LLM judge or API calls.-
Glama MCP Gateway
Add one secure layer between your agents and this server.