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.
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Average 4.8/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: check_text analyzes text for issues, fix_duplicates handles a specific correction, and list_word_lists provides configuration details. There is no overlap or confusion between analysis, fixing, and introspection.
All three tool names follow a consistent verb_noun pattern: check_text, fix_duplicates, list_word_lists. This makes the API predictable and easy to navigate.
The server uses only 3 tools, which is minimal but acceptable for a focused writing style checker. It covers core analysis plus one fix and one introspection tool, though the surface feels slightly thin.
The server provides a solid core check function and a duplicate fixer, but lacks configuration tools, file input (on the hosted server), and fixes for other issue types. These are notable gaps that limit its usefulness for full writing style management.
check_textCheck text for writing style issuesAAnalyze 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description details privacy (never stored), return format (plain-text report with line/column, context, reason for AI tells), an error condition (>100,000 characters), and lack of filesystem access. This adds rich behavioral context beyond the structured 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?
The description is compact yet information-dense, with the core purpose front-loaded. Each sentence adds value: behavior, privacy, return details, limits, alternatives, and tool relationship. No words are wasted.
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 absence of an output schema, the description fully explains return format and error behavior. It also covers side effects (none), scope (hosted server, no filesystem), and alternative tools. This is complete for the tool's complexity and context.
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 coverage is 100% so baseline is 3, but the description adds a meaningful constraint not present in the schema: texts over 100,000 characters return an error. This clarifies the text parameter's limits. It also reinforces the config's purpose and format's effect without redundancy.
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 a specific verb ('Analyze text for writing style issues') and enumerates the exact issue types (weasel words, passive voice, etc.), distinguishing it from sibling tools. It also explicitly contrasts with fix_duplicates by noting this tool only reports issues.
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?
It gives clear usage context: read-only, stateless, works on in-memory text, and explicitly points to fix_duplicates for automatic removal and to a separate check_file tool for local files. This provides direct alternatives and exclusion guidance.
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 wordsARemove 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses behavioral details beyond annotations: case-insensitivity, handling across line breaks, and the return of cleaned text plus removed words. The 'Read-only with no side effects' statement reinforces readOnlyHint and clarifies no external writes, adding value beyond structured data.
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 concise sentences, each with a distinct purpose: action, safety, and usage context. No redundant or irrelevant information, making it efficient and well-structured.
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 no output schema, the description covers all necessary aspects: purpose, return value, read-only nature, and usage context. It is complete for an agent to select and invoke correctly.
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?
The input schema fully describes the only parameter ('text') with 100% coverage. The tool description does not add extra semantic detail about the parameter itself, so the baseline score of 3 is appropriate.
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 tool's function: 'Remove duplicate adjacent words' with specific details (case-insensitive, across line breaks). It distinguishes itself from siblings by positioning as the fix tool after check_text reports duplicates, while listing other issue types as report-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 states when to use: 'Use after check_text reports duplicate words' and implicitly when not to use: 'other issue types are report-only and have no auto-fix.' This provides clear guidance relative to alternatives.
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 listsAReturn 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 | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only safety. The description adds that it returns the same catalog for a given release, indicating determinism, and details the output contents. It doesn't contradict annotations, and the added deterministic/release-bound behavior goes beyond the annotation.
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 three sentences: the first states the core purpose and output, the second adds determinism and read-only nature, the third gives usage guidance. No filler words.
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 simple zero-parameter tool and existing annotations, the description is complete. It describes the return value in detail (entry count, config key, samples, link) and provides usage context, which is sufficient since no output schema exists.
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?
The tool takes no parameters, so the baseline is 4. The description correctly notes this and adds no conflicting or extra parameter information, which is appropriate for a zero-parameter tool.
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 it returns every detector word/phrase list with entry count, config key, sample entries, and a link to the library. It differentiates from siblings like check_text by noting its use for inspecting match rules before tuning configs.
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 states when to use: 'before tuning a config for check_text', and when not needed: 'not needed for ordinary checking'. This provides clear guidance on its role versus alternative tools.
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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