Official Model Context Protocol (MCP) server for VerbaShield AI — AI text detection, text humanization, and quota management for AI coding agents and assistants (Cursor, Claude Desktop, Antigravity, Windsurf, Cline).
verbashield_humanize: Rewrite and humanize AI-generated text into natural human Russian (modes: academic, business, casual).
verbashield_detect: Analyze Russian/multilingual text for AI probability, sentence breakdown, perplexity, and burstiness.
verbashield_get_quota: Check remaining character quota, balance, and rate limits.
Related MCP server: humantone-mcp
Add to your claude_desktop_config.json:
{
"mcpServers": {
"verbashield": {
"command": "npx",
"args": ["-y", "@dalexsys/verbashield-mcp"],
"env": {
"VERBASHIELD_API_KEY": "vs_live_your_api_key_here"
}
}
}
}In Cursor Settings → Features → MCP Servers → Add New MCP Server:
Name: verbashield
Type: command
Command: env VERBASHIELD_API_KEY=vs_live_your_api_key_here npx -y @dalexsys/verbashield-mcp
Log in to verbashield.ru.
Go to Developer Settings or visit verbashield.ru/docs.
Create your API Key (vs_live_...).
verbashield_humanizeRewrites text to remove AI markers and clichés.
text (string, required): Original text to humanize.
mode (string, optional): academic (default), business, or casual.
preserve_formatting (boolean, optional): Keep Markdown/paragraphs intact.
verbashield_detectDetects AI-generated content.
text (string, required): Text to evaluate.
verbashield_get_quotaReturns remaining monthly characters and rate limits.
MIT © VerbaShield AI
Detect AI-generated text via VerbaShield
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided and description only states the core purpose. Does not disclose output format, input length limits, model constraints, or any other behavioral traits. Minimal.
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?
Single sentence, no waste, concise while stating purpose.
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 detection API, crucial context is missing: what the response looks like (probability, label, confidence), whether there are length limits. No annotations and no output schema, so the description carries the burden. Sibling names help, but not enough.
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?
Single 'text' parameter has no schema description (0% coverage), and description adds no guidance on expected length, language, or content. Only tells the agent that text is required.
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 specific verb 'Detect' and resource 'AI-generated text via VerbaShield'. Clearly distinguishes from sibling verbashield_humanize which transforms text. No detailed scope but adequate.
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?
Implied usage via 'Detect' and the sibling names, but no explicit when-to-use guidance or when-not-to-use. Agent can infer it's for detection versus humanization, but no explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return remaining VerbaShield quota, bonus balance, and rate limit
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The verb 'Return' clearly signals a read-only operation, and the description states the three pieces of data returned. It does not disclose output formats or units, but for a parameterless read tool this is reasonably transparent.
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 concise sentence that front-loads the action and the returned data. There is no redundant wording or filler.
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 parameterless tool, the description is largely sufficient: an agent can invoke it immediately and knows what to expect in the response. Minor gaps such as value formats or units are not critical for correct invocation.
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 has zero parameters, so the baseline is 4. There is no parameter information needed in the description; the empty schema already conveys that no arguments are required.
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 uses a specific verb ('Return') and names the exact data exposed: remaining VerbaShield quota, bonus balance, and rate limit. It is clearly distinguishable from sibling tools 'verbashield_humanize' and 'verbashield_detect', which imply text transformation and detection rather than quota lookup.
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 intended use is implied: call this tool when the agent needs quota, bonus, or rate-limit information. However, the description provides no explicit guidance about when to use it vs alternatives, nor any prerequisites or conditions that indicate the right time to call it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Humanize AI-generated text via VerbaShield
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | ||
| text | Yes | ||
| preserve_formatting | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does not explain whether text is rewritten and returned, how formatting preservation works, whether any rate limits or auth constraints apply, or what side effects exist. Only the basic operation is stated.
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 and has no wasted words, but it is under-specified for a tool with three parameters and no output schema. It is concise rather than wisely structured, lacking essential semantic layers.
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 lack of annotations, zero schema description coverage, and no output schema, the description is severely incomplete. It does not explain mode behavior, formatting preservation, return value shape, or operational constraints, so an agent cannot confidently invoke it with correct parameters.
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 0%, and the description provides no explanation of the three parameters. It does not clarify what 'mode' values mean, what 'text' expects, or what 'preserve_formatting' controls, leaving agents with only raw type/enum information.
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 states a specific action ('humanize') applied to a clear resource ('AI-generated text'). This distinguishes it from siblings like verbashield_detect and verbashield_get_quota, though not explicitly; the verb itself makes the core purpose clear.
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?
No guidance is given on when to choose this tool over alternatives, and there are no exclusions or prerequisite conditions. The name and description only imply a use case; nothing tells the agent how this relates to verbashield_detect or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.1verbashield_detectverbashield_get_quotaverbashield_humanizeScored across 3 tools
Each tool performs a distinct function: humanize transforms text, detect analyzes text, and get_quota retrieves account usage. There is no overlap or ambiguity between the three tools.
All tools share the verbashield_ prefix and use lowercase snake_case. The pattern is consistent and predictable: verbashield_humanize, verbashield_detect, and verbashield_get_quota.
Three tools is well-scoped for a narrow service wrapper around VerbaShield, covering the primary actions and account quota without unnecessary bulk.
The tool set covers the full lifecycle for this service: text transformation, detection, and quota monitoring. No obvious gaps exist for the stated domain.
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