WSC - Writing Style Checker
Server Details
Writing Style Checker (WSC) is a prose linter with an AI-tells detector: alongside classic checks (weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs) it flags 190+ research-cited words, phrases, and structural patterns overrepresented in AI-generated text — each with an explanation and source.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.5/5 across 3 of 3 tools scored.
Each tool targets a distinct function: check_text analyzes text for multiple style issues, fix_duplicates removes duplicate words, and list_word_lists provides configurable word lists. No overlap in purpose.
All tool names follow a consistent verb_noun pattern (check_text, fix_duplicates, list_word_lists) using snake_case, making them predictable and easy to distinguish.
With 3 tools, the server is well-scoped for its purpose: core analysis (check_text), a specific fix (fix_duplicates), and configuration insight (list_word_lists). No superfluous or missing tools relative to the domain.
The set covers detection comprehensively but only provides an automated fix for duplicate words; other detected issues (passive voice, weasel words, etc.) have no corresponding fix tool, creating a notable gap for practical use.
Available Tools
3 toolscheck_textCheck text for writing style issuesARead-onlyInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to analyze for writing style issues | |
| config | No | Optional config to enable/disable detectors or add/remove word-list entries; same schema as .wscrc.json (https://wsc.theserverless.dev/schema.json) | |
| format | No | Set to "markdown" to mask code blocks, inline code, tables, and headings so they are not linted as prose; default "plain" lints everything |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds value by specifying stateless operation, in-memory analysis with no storage, error handling for long texts, and that the tool only reports issues (not fixing them). This goes beyond what annotations provide.
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?
The description is a single paragraph that efficiently covers purpose, capabilities, privacy, error behavior, and alternatives. It is front-loaded with the main use case and is fairly concise given the amount of information.
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?
Given the tool's complexity (3 params, nested config object, no output schema), the description provides sufficient context: return format, error conditions, privacy, and limitations. It helps the agent understand when to use the tool and what to expect.
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?
Schema description coverage is 100%, so baseline is 3. The description does not add significant parameter-specific details beyond what the schema already provides, but it does give context like the error message for long texts. Overall, it meets the baseline.
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?
The description clearly states 'Analyze text for writing style issues' and lists specific issue types, using a specific verb-resource combination. It distinguishes from sibling tools by naming fix_duplicates and check_file.
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?
The description provides context for when to use (analyze text) and mentions alternatives for local files (check_file) and fixing duplicates (fix_duplicates). While it doesn't explicitly state when not to use, the guidance is clear enough for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fix_duplicatesRemove duplicate adjacent wordsARead-onlyInspect
Remove duplicate adjacent words (case-insensitive, including across line breaks) and return the cleaned text plus the list of words that were removed. Read-only with no side effects: the fix is returned in the response, nothing is written anywhere. Use after check_text reports duplicate words; other issue types are report-only and have no auto-fix.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to clean by removing duplicate adjacent words |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds behavioral detail beyond readOnlyHint annotation, stating no side effects (nothing written) and that the fix is returned in the response. Also clarifies case-insensitivity and line-break handling. Minor gap: no mention of error conditions or limitations.
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?
Two sentences, no wasted words, front-loaded with action and key details. Every sentence earns its place.
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?
For a simple tool with one parameter and no output schema, the description fully covers what the tool does, how it works, its return, and usage context. Nothing missing.
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?
Schema covers 100% with basic description; description enhances parameter meaning by specifying case-insensitive and across-line-breaks behavior, adding value beyond schema.
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?
Clearly states the tool removes duplicate adjacent words with specific details (case-insensitive, across line breaks) and returns cleaned text and removed list. Distinguishes from sibling check_text by noting it is used for auto-fix of duplicate words only.
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?
Explicitly advises to use after check_text reports duplicate words and warns that other issue types are report-only with no auto-fix. Provides clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_word_listsList detector word listsARead-onlyInspect
Return every detector word/phrase list with its entry count, config key, and sample entries, plus a link to the full browsable library. Read-only, takes no parameters, and returns the same catalog for a given release. Use it to see what the detectors match before tuning a config for check_text; not needed for ordinary checking.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already include readOnlyHint=true. Description adds that it is read-only, takes no parameters, and returns the same catalog per release. No contradictions and adds useful behavioral context beyond annotations.
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?
Two sentences: first describes functionality, second gives usage context. No unnecessary words. Front-loaded with key information.
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, but description explains what is returned (entry count, config key, sample entries, link to library). Also relates to sibling tools, providing complete context for selection and use.
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?
Input schema has zero parameters, so description stating 'takes no parameters' is redundant but confirms. Schema coverage is 100%, so baseline is 3. Description adds minimal semantic value beyond schema.
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?
The description clearly states the verb 'return' and specifies the resource 'detector word/phrase list' with details like entry count, config key, and sample entries. It distinguishes itself from siblings (check_text, fix_duplicates) by focusing on listing, not checking or fixing.
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?
Explicitly advises to use this tool to see what detectors match before tuning config for check_text, and clarifies it's not needed for ordinary checking. Provides clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!