Skip to main content
Glama

Writing Style Checker

by io.github.theserverlessdev

Check text for writing style issues

check_text
Read-only

Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to analyze for writing style issues
configNoOptional config to enable/disable detectors or add/remove word-list entries; same schema as .wscrc.json (https://wsc.theserverless.dev/schema.json)
formatNoSet to "markdown" to mask code blocks, inline code, tables, and headings so they are not linted as prose; default "plain" lints everything

Schema Changelog

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

  1. Changed2 schema fields changed
    • changedInput schema / properties / config / description
      Previous value: -"Optional WscConfig to customize which detectors run and their settings"New value: +"Optional config to enable/disable detectors or add/remove word-list entries; same schema as .wscrc.json (https://wsc.theserverless.dev/schema.json)"
    • changedInput schema / properties / format / description
      Previous value: -"Set to \"markdown\" to skip code blocks, tables, and headings when analyzing"New value: +"Set to \"markdown\" to mask code blocks, inline code, tables, and headings so they are not linted as prose; default \"plain\" lints everything"
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Discloses read-only and stateless nature, in-memory processing, no storage, return format details, error condition, and lack of filesystem access. Annotations already indicate read-only, but description adds comprehensive behavioral context.

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?

Well-structured and front-loaded with purpose. Slightly verbose but each sentence adds value. No redundancy, but could be marginally shortened without losing information.

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?

For a tool with 3 parameters (one nested object) and no output schema, the description fully explains return format (plain-text report with specific fields), error handling (over 100k chars), and constraints (no filesystem access). Completely sufficient for agent decision-making.

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 coverage is 100%, so baseline is 3. Description does not add significant new meaning for parameters; it references the config schema link but the schema already provides descriptions. No additional clarity beyond schema.

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?

Clearly states it analyzes text for writing style issues, listing specific issue types. Distinguishes from sibling tools (fix_duplicates, check_file) directly.

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

Usage Guidelines5/5

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

Explicitly mentions when to use check_file for local files and fix_duplicates for auto-removal. Also notes the 100k character limit and error behavior, providing clear contextual guidance.

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

All Writing Style Checker toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a unique, clearly defined purpose: check_text analyzes text for various issues, fix_duplicates specifically removes duplicate words, and list_word_lists provides metadata about the detection lists. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case (check_text, fix_duplicates, list_word_lists), making them predictable and easy to distinguish.

Tool Count5/5

Three tools is an appropriate count for a focused writing style checker: analysis, one targeted fix, and introspection. The number feels neither too sparse nor excessive for the domain.

Completeness4/5

The set covers the core use case of detecting writing issues and provides one automated fix (duplicates) plus lookups of detection rules. Missing are auto-fixes for other issue types and a tool to configure detectors, but the descriptions explicitly note that only duplicates have auto-fix, so the surface is intentionally scoped.

Resources