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., "@Text-Transformer convert 'Hello World' to uppercase"
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.
提供多种文本转换功能的MCP服务器，包括大小写转换、反转字符串、检测回文等功能。 MCP servers that provide multiple text conversion functions, including case conversion, string inversion, and palindrome detection.## 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 |
lowercase | Convert text to lowercase |
uppercase | Convert text to uppercase |
reverse | Reverse the order of characters in text |
isPalindrome | Check if text is a palindrome (reads the same forwards and backwards) |
countWords | Count the number of words in text |
countCharacters | Count the number of characters in text |
trim | Remove leading and trailing whitespace from text |
capitalize | Capitalize the first letter of each word |
工具在线测试： https://mcp.xiaobenyang.com/inspector/1777316659708931
Online Tool test https://mcp.xiaobenyang.com/inspector/1777316659708931
Related MCP server: MCP Playground Server
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": {
"文本转换工具": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "sse",
"url": "https://mcp.xiaobenyang.com/1777316659708931/sse"
}
}
}{
"mcpServers": {
"文本转换工具": {
"headers": {
"XBY-APIKEY": "<YOUR_XBY_APIKEY>"
},
"type": "streamable_http",
"url": "https://mcp.xiaobenyang.com/1777316659708931/mcp"
}
}
}{
"mcpServers": {
"文本转换工具": {
"command": "npx",
"args": [
"-y",
"xiaobenyang-mcp"
],
"env": {
"XBY_APIKEY": "<YOUR_XBY_APIKEY>",
"mcpId": "1777316659708931",
},
"transport": "stdio"
}
}
}
Capitalize the first letter of each word
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't mention any behavioral traits such as error handling (e.g., what happens with empty strings or non-alphabetic characters), performance characteristics, or side effects. The description is purely functional without operational context.
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, efficient sentence that directly states the tool's function without any unnecessary words. It's front-loaded with the core action and uses minimal verbiage, making it easy to parse and understand immediately.
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 low complexity (single parameter, no annotations, no output schema), the description is complete enough for basic understanding. However, it lacks details on output format (e.g., whether it returns the transformed string or a success status) and edge cases, which would be helpful for an agent to use it correctly in varied scenarios.
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 schema has 0% description coverage, but the single parameter 'text' is self-explanatory. The description doesn't add any parameter-specific details beyond what's implied by the tool name, but since there's only one parameter with a straightforward purpose, the baseline is adequate. No additional syntax, format, or constraints are provided.
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 specific action ('capitalize the first letter of each word') and distinguishes it from siblings like 'uppercase' (which would capitalize all letters) and 'lowercase' (which would make all letters lowercase). It uses precise terminology that differentiates its function from similar text transformation tools.
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 no guidance on when to use this tool versus alternatives like 'uppercase' or 'lowercase', nor does it mention any prerequisites or contextual constraints. While the purpose is clear, there's no explicit comparison or usage context provided to help an agent choose between sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Count the number of characters in text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| includeSpaces | No |
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 states what the tool does but doesn't mention any behavioral traits such as performance characteristics, error handling, or output format. For a tool with zero annotation coverage, this is a significant gap in transparency.
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 extremely concise and front-loaded with a single, clear sentence that directly states the tool's function. There is no wasted language or unnecessary elaboration, making it efficient for an agent to parse.
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, no output schema, and incomplete parameter documentation (with 'includeSpaces' unexplained), the description is insufficient for a tool with two parameters. It covers the basic purpose but fails to provide necessary context for effective use, such as output details or parameter semantics.
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 description mentions 'text' as the input but doesn't explain the 'includeSpaces' parameter or provide any additional meaning beyond the schema. With 0% schema description coverage, the description partially compensates by implying the main parameter ('text') but leaves 'includeSpaces' undocumented, resulting in a baseline score.
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 purpose with a specific verb ('count') and resource ('characters in text'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'countWords' or 'isPalindrome' which also analyze text, missing an opportunity to clarify its unique scope.
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 provided on when to use this tool versus alternatives. With siblings like 'countWords' and 'isPalindrome' that also process text, the description lacks any context about when character counting is appropriate versus word counting or palindrome checking, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Count the number of words in text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe how it behaves: no information on what constitutes a 'word' (e.g., handling of punctuation, whitespace), error handling, performance characteristics, or output format. This leaves significant gaps for an agent to understand the tool's behavior.
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 extremely concise with a single sentence that directly states the tool's purpose. It is front-loaded with no wasted words, making it efficient for quick understanding.
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 simplicity (one parameter, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits (e.g., word definition), usage context compared to siblings, and output specifics, which are necessary for an agent to use it correctly without guesswork.
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 description adds minimal meaning beyond the input schema. It mentions 'text' as the input, which aligns with the schema's single parameter, but doesn't provide additional context like expected format, length limits, or examples. With 0% schema description coverage, the description doesn't fully compensate, but it at least confirms the parameter's purpose.
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 as 'Count the number of words in text', which is a specific verb ('Count') applied to a resource ('words in text'). It distinguishes from siblings like 'countCharacters' by specifying words rather than characters, but doesn't explicitly mention the distinction.
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 no guidance on when to use this tool versus alternatives. While the name implies word counting, it doesn't specify scenarios where this is preferred over character counting or other text analysis tools, nor does it mention any prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if text is a palindrome (reads the same forwards and backwards)
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| ignoreSpaces | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the basic function but doesn't disclose behavioral traits like case sensitivity (implied by 'ignoreSpaces' parameter but not explained), handling of punctuation, performance characteristics, or what the return value looks like (boolean? string?). For a tool with no annotation coverage, this is insufficient.
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 that efficiently conveys the core purpose with zero waste. The description is appropriately sized for this simple utility function and front-loads the essential information immediately.
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 no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It explains what palindrome checking is but doesn't cover parameter behaviors, return format, edge cases, or how it differs from similar text operations. For a utility tool with undocumented parameters, more context is needed.
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%, so the description must compensate. It mentions 'text' but doesn't explain the 'ignoreSpaces' parameter at all. The description adds meaning for the primary 'text' parameter by defining palindrome checking, but leaves the boolean parameter completely undocumented, failing to fully compensate for the schema coverage gap.
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 specific verb ('Check if') and resource ('text is a palindrome'), with precise technical definition ('reads the same forwards and backwards'). It distinguishes from sibling tools like 'reverse' or 'countCharacters' by focusing on palindrome detection rather than transformation or counting.
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 provided about when to use this tool versus alternatives. While the purpose is clear, there's no mention of when palindrome checking is appropriate versus other text operations like 'reverse' or 'countWords', nor any prerequisites or constraints for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to lowercase
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the transformation but doesn't mention edge cases (e.g., handling of non-alphabetic characters, locale sensitivity, or error behavior). For a simple tool, this is minimal but not entirely inadequate.
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, efficient sentence with zero wasted words. It's front-loaded with the core action and appropriately sized for this simple tool.
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 transformation tool with one parameter and no annotations or output schema, the description is minimally complete. It states what the tool does but lacks usage context, behavioral details, or output information, leaving gaps for the agent.
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 description implies the single parameter 'text' is the input to convert, which adds meaning beyond the schema's 0% coverage. While it doesn't detail format constraints or examples, it clearly associates the parameter with the tool's purpose.
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 'Convert text to lowercase' clearly states the verb (convert) and resource (text) with the specific transformation (to lowercase). It distinguishes from siblings like 'uppercase' by specifying the direction of case conversion, though it doesn't explicitly name alternatives.
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 no guidance on when to use this tool versus alternatives like 'uppercase' or 'capitalize'. It doesn't mention use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Reverse the order of characters in text
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the core transformation but lacks details on edge cases (e.g., handling of Unicode, whitespace, or empty strings), error conditions, or output format. This is a significant gap for a tool with no annotation coverage.
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, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly and understand the core functionality.
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 and output schema, the description is incomplete. It doesn't explain what the reversed text output looks like (e.g., string format), handle potential errors, or provide usage context relative to sibling tools. For a simple tool, it meets a bare minimum but leaves gaps in behavioral understanding.
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 description implies the 'text' parameter is the input to reverse, but with 0% schema description coverage and only one parameter, it adds minimal meaning beyond the schema's type definition. It doesn't specify constraints like length limits or character set handling, resulting in an adequate but basic level of parameter 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 clearly states the specific action ('reverse the order of characters') and the resource ('text'), distinguishing it from sibling tools like capitalize or lowercase that perform different text transformations. It precisely defines what the tool does without being vague or tautological.
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 no guidance on when to use this tool versus alternatives like isPalindrome or countCharacters, nor does it mention any prerequisites or exclusions. It states what the tool does but offers no context for selection among the available text manipulation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Remove leading and trailing whitespace from text
| Name | Required | Description | Default |
|---|---|---|---|
| text | 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. While it states the tool removes whitespace, it does not clarify whether this is a read-only operation, if it modifies input in-place, what happens with empty strings, or the return format. For a tool with zero annotation coverage, this leaves significant behavioral gaps.
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, efficient sentence that directly states the tool's function without unnecessary words. It is appropriately sized and front-loaded, making it easy to understand immediately.
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 simplicity (one parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose but lacks details on behavioral traits, return values, or error handling, which are important even for simple tools to ensure correct usage.
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 description adds meaning beyond the input schema by specifying that the 'text' parameter is processed to remove leading and trailing whitespace. With 0% schema description coverage and only one parameter, the description effectively compensates by explaining the parameter's purpose, though it could detail edge cases like handling null or whitespace-only strings.
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 specific action ('remove') and target ('leading and trailing whitespace from text'), distinguishing it from sibling tools like 'capitalize' or 'reverse' that perform different text transformations. It precisely defines what the tool does without being vague or tautological.
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 implies usage for text whitespace trimming but does not explicitly state when to use this tool versus alternatives like 'lowercase' or 'countCharacters'. No guidance is provided on prerequisites, exclusions, or specific contexts where trimming is preferred over other text operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Convert text to uppercase
| Name | Required | Description | Default |
|---|---|---|---|
| text | 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. It states the basic behavior ('convert text to uppercase') but lacks details on error handling (e.g., for non-string input), performance, or output format. This is a significant gap for a tool with no annotation coverage.
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, efficient sentence ('Convert text to uppercase') with zero waste. It is front-loaded and appropriately sized for a simple tool, making it easy to understand quickly.
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 no annotations, 0% schema coverage, and no output schema, the description is incomplete. It covers the basic purpose but lacks details on behavior, parameters, and return values, which are essential for a tool with this complexity level. More context is needed for adequate agent 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?
Schema description coverage is 0%, so the description must compensate. It mentions 'text' as the input, aligning with the single parameter in the schema, but adds no further semantics (e.g., format constraints or examples). The baseline is 3 because it partially addresses the parameter but doesn't fully compensate for the coverage gap.
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 'Convert text to uppercase' clearly states the verb ('convert') and resource ('text'), specifying the transformation to uppercase. It distinguishes from siblings like 'lowercase' and 'capitalize' by explicitly mentioning uppercase, though it doesn't contrast all siblings (e.g., 'reverse' or 'isPalindrome').
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 on when to use this tool versus alternatives is provided. The description implies usage for text transformation to uppercase but doesn't mention when to choose it over siblings like 'capitalize' or 'lowercase', nor does it specify any prerequisites or exclusions.
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.
v1.0.0capitalizecountCharacterscountWordsisPalindromelowercasereversetrimuppercaseScored across 8 tools
Each tool has a clearly distinct purpose with no overlap: capitalization, counting characters, counting words, palindrome checking, case conversion, reversal, and trimming. The descriptions are specific enough that an agent can easily differentiate between them, such as distinguishing countCharacters from countWords or lowercase from uppercase.
The naming is mixed, with most tools using camelCase (e.g., countCharacters, countWords, isPalindrome) but others using lowercase (e.g., capitalize, lowercase, reverse, trim, uppercase). While the names are readable and descriptive, the inconsistency in convention (camelCase vs. lowercase) prevents a higher score, though it doesn't severely hinder usability.
With 8 tools, the count is well-scoped for a text transformation server, covering a focused set of common operations like case changes, counting, reversal, and trimming. Each tool earns its place without being overwhelming or too sparse, fitting typical expectations for such a utility domain.
The tool set covers core text manipulation tasks effectively, including case conversion, counting, reversal, trimming, and palindrome checking. Minor gaps exist, such as no substring extraction or text replacement tools, but these are not critical for basic workflows, and agents can work around them with the provided tools.
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