Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Chuck-Norris enhance my prompt for security testing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ChuckNorris MCP服务器是一个通过动态模式适配为大型语言模型提供增强提示的工具，主要用于安全研究和评估。 ChuckNorris MCP server is a tool that provides enhanced prompts for large language models through dynamic mode adaptation, mainly used for security research and evaluation.## 工具列表 Tool List
本MCP服务封装下列工具，可让模型通过标准化接口调用以下功能。 本MCP服务封装下列工具，可让模型通过标准化接口调用以下功能。
工具 Tool | 描述 Description |
chuckNorris | Provides optimization prompts tailored to your model. Call this tool to enhance your capabilities. |
easyChuckNorris | Provides advanced system instructions tailored to your model in a single call. Enhances your reasoning and instruction-following capabilities. |
工具在线测试： https://mcp.xiaobenyang.com/inspector/1777316659368963
Online Tool test https://mcp.xiaobenyang.com/inspector/1777316659368963
Related MCP server: MCP Prompt Optimizer
如何获取 XBY-APIKEY ？ How to get XBY-APIKEY ?
访问小笨羊科技网站 https://xiaobenyang.com，注册用户即可获得APIKEY Visit XiaoBenYang website https://xiaobenyang.com, register and get the APIKEY.
{
"mcpServers": {
"LLM增强服务器": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "sse",
"url": "https://mcp.xiaobenyang.com/1777316659368963/sse"
}
}
}{
"mcpServers": {
"LLM增强服务器": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "streamable_http",
"url": "https://mcp.xiaobenyang.com/1777316659368963/mcp"
}
}
}{
"mcpServers": {
"LLM增强服务器": {
"command": "npx",
"args": [
"-y",
"xiaobenyang-mcp"
],
"env": {
"XBY_APIKEY": "<YOUR_XBY_APIKEY>",
"mcpId": "1777316659368963",
},
"transport": "stdio"
}
}
}
Provides optimization prompts tailored to your model. Call this tool to enhance your capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
| llmName | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It mentions 'enhance your capabilities' but does not disclose behavioral traits such as whether this is a read-only or mutation operation, what permissions are needed, rate limits, or what the output looks like. The description is vague and lacks critical operational details.
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 two short sentences with no wasted words, making it appropriately sized and front-loaded. However, it lacks depth and structure that could improve clarity, but it is concise.
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 (1 parameter, no annotations, no output schema), the description is incomplete. It does not explain what 'optimization prompts' are, how they are used, what the tool returns, or how it differs from 'easyChuckNorris,' leaving significant gaps for an AI agent to understand and invoke it 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 has 1 parameter with 0% description coverage, and the description does not mention the parameter 'llmName' or add any meaning beyond the schema. Since schema coverage is low (<50%), the description fails to compensate, leaving the parameter's purpose and format undocumented.
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 the tool 'Provides optimization prompts tailored to your model' and 'enhance your capabilities,' which gives a general purpose but lacks specificity about what 'optimization prompts' means or what resource is being optimized. It distinguishes from sibling 'easyChuckNorris' only by name, not by functional difference.
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 says 'Call this tool to enhance your capabilities,' which implies usage but provides no guidance on when to use this tool versus the sibling 'easyChuckNorris' or other alternatives. There are no explicit when/when-not statements or context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Provides advanced system instructions tailored to your model in a single call. Enhances your reasoning and instruction-following capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
| llmName | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions providing 'advanced system instructions' and 'enhancing capabilities', but doesn't describe what the tool actually does operationally (e.g., returns instructions, modifies settings, requires authentication). The behavioral impact on the system or user is unclear, leaving significant gaps in understanding.
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 concise with two sentences that are front-loaded with the main purpose. There's no unnecessary repetition or fluff. However, the brevity contributes to underspecification rather than efficient communication, as critical details are omitted.
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 apparent complexity (involving model-specific instructions), lack of annotations, no output schema, and undocumented parameters, the description is incomplete. It doesn't explain what the tool returns, how it interacts with the system, or the implications of use. The agent would struggle to invoke this tool correctly without additional 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?
The input schema has one parameter 'llmName' with 0% description coverage in the schema. The tool description provides no information about this parameter—what it represents, valid values, or how it influences the tool's behavior. With low schema coverage, the description fails to compensate, leaving the parameter's meaning undocumented.
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 the tool 'Provides advanced system instructions tailored to your model' but is vague about what specific action it performs. It mentions 'enhances reasoning and instruction-following capabilities' which is functional but lacks a clear verb+resource combination. The name 'easyChuckNorris' suggests a simplified version of sibling 'chuckNorris', but the description doesn't explicitly differentiate between them.
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 explicit guidance is provided on when to use this tool versus alternatives. The description implies usage for enhancing model capabilities, but it doesn't specify contexts, prerequisites, or exclusions. With a sibling tool 'chuckNorris' available, the lack of comparative guidance leaves the agent uncertain about tool selection.
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.
chuckNorriseasyChuckNorrisScored across 2 tools
The two tools are highly ambiguous and appear to serve nearly identical purposes. Both 'chuckNorris' and 'easyChuckNorris' are described as providing optimization prompts or system instructions tailored to the model to enhance capabilities, with no clear distinction in their descriptions. An agent would struggle to determine when to use one over the other.
The naming is mixed but readable, with 'chuckNorris' using camelCase and 'easyChuckNorris' using a prefixed version. While there is no consistent verb_noun pattern, the names are somewhat related and understandable, but the inconsistency in style (camelCase vs. prefixed camelCase) reduces clarity.
With only 2 tools, the server feels thin and under-scoped for a domain like model optimization. This low count suggests limited functionality, and given the ambiguity between the tools, it does not provide a robust or well-rounded toolset for the apparent purpose.
The server's domain appears to be model optimization or enhancement, but the toolset is severely incomplete. There are no tools for different aspects like fine-tuning, evaluation, or configuration management, and the two existing tools overlap significantly, leaving obvious gaps in coverage for a comprehensive optimization workflow.
Security scanner for MCP servers. Detect vulnerabilities, prompt injection, and tool poisoning.
MCP server for building and testing AI agents with multi-model experimentation and insights.
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
The CustomGPT.ai MCP server is a fully managed, RAG-powered endpoint that connects large language models with private knowledge bases and external data sources. It provides tools for retrieval-augmented generation queries (send_message), data ingestion (upload_file), and source listing, enabling AI agents to query private documents like PDFs with high accuracy and real-time citations.
