El Protocolo de Contexto de Modelo (MCP) es un protocolo estandarizado diseñado para gestionar el contexto entre modelos de lenguaje grandes (LLM) y sistemas externos.
El servidor Chargebee MCP ofrece un conjunto robusto de herramientas para mejorar la eficiencia del desarrollador. Se integra con editores de código basados en IA como Cursor, Windsurf y Cline, así como con herramientas de uso general como Claude Desktop. Es compatible con cualquier cliente MCP.
Con este servidor MCP, puedes:
Obtenga respuestas inmediatas sobre los productos y servicios API de Chargebee.
Reciba fragmentos de código sensibles al contexto adaptados a sus necesidades de integración.
Acceda a la base de conocimientos de Chargebee, que incluye:
Documentación
Preguntas frecuentes
Notas de la versión
Y mucho más
Node.js LTS : el servidor Chargebee MCP requiere la versión Node.js LTS para funcionar correctamente.
Related MCP server: Codinfy MCP
Para ejecutar el servidor Chargebee MCP usando Node.js npx , ejecute el siguiente comando:
npx -y @chargebee/mcp@latestHerramienta | Descripción |
| Busque en la documentación de Chargebee para obtener información detallada y guías de uso. |
| Genere esquemas de código estructurados y códigos de muestra para integrar las API y funciones de Chargebee. |
Para agregar este servidor a Cursor IDE:
Vaya a Cursor Settings > MCP .
Haga clic en + Add new Global MCP Server .
Agregue la siguiente configuración a su archivo global .cursor/mcp.json :
{
"mcpServers": {
"chargebee": {
"command": "npx",
"args": [
"-y",
"@chargebee/mcp"
]
}
}
}Consulta la documentación de Cursor para obtener más información. Nota: También puedes añadir esto a la configuración de Cursor específica de tu proyecto (compatible con Cursor 0.46 y versiones posteriores).
Para configurar MCP con Cascade:
Abra Windsurf y vaya a Settings > Advanced Settings o use la Paleta de comandos > Open Windsurf Settings Page .
Desplácese hasta la sección Cascada para agregar un nuevo servidor, ver servidores existentes o acceder al archivo de configuración JSON sin procesar en mcp_config.json .
Haga clic en “Agregar servidor personalizado +” para incluir el servidor Chargebee MCP directamente en mcp_config.json :
{
"mcpServers": {
"chargebee": {
"command": "npx",
"args": [
"-y",
"@chargebee/mcp"
]
}
}
}Consulte la documentación de Windsurf para obtener más información.
Agregue manualmente el siguiente JSON a su archivo cline_mcp_settings.json a través de la configuración del servidor Cline MCP:
{
"mcpServers": {
"chargebee": {
"command": "npx",
"args": [
"-y",
"@chargebee/mcp"
]
}
}
}Acceda a la configuración de MCP seleccionando Edit MCP Settings en la configuración de Roo Code o usando el Roo Code: Open MCP Config en la paleta de comandos de VS Code:
{
"mcpServers": {
"chargebee": {
"command": "npx",
"args": [
"-y",
"@chargebee/mcp"
]
}
}
}Agregue la siguiente configuración a su archivo claude_desktop_config.json . Consulte la documentación de Claude Desktop para obtener más información:
{
"mcpServers": {
"chargebee": {
"command": "npx",
"args": [
"-y",
"@chargebee/mcp"
]
}
}
}También puede ejecutar el servidor como una CLI ejecutando el siguiente comando:
npx -y @chargebee/mcp@latestPara contribuir a este proyecto, consulte la guía de contribución .
Use this tool for any Chargebee integration questions or implementation needs.
Always use this tool to get the accurate integration code guide for Chargebee. This is the main tool developers need when asking about implementing Chargebee functionality (like "how to update billing address", "how to create subscription", "how to handle webhooks", etc.).
This tool will take in parameters about integrating with Chargebee in their application and generates an integration workflow along with the code snippets.
It takes the following arguments:
goal (string): What is the user's goal?
language (enum): Programming language the code to be generated in. Check the user's application language.
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | What is the user's goal? | |
| language | No | Programming language the code to be generated in. Check the user's application language. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only describes the tool as generating code but does not clarify that it is a read-only operation with no side effects or data mutations. Such information is essential for safe invocation.
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 contains redundant phrasing (e.g., 'Use this tool' and 'Always use this tool') and could be more streamlined. However, it is not excessively long and includes necessary details.
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?
The description covers the tool's inputs and high-level output (workflow and code snippets) but lacks details on the output structure, error handling, or limitations. With no output schema, more specifics would improve completeness.
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%, and the descriptions in the schema are adequate. The tool description reiterates the parameter meanings without adding new context or examples, meeting the baseline for high coverage.
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: generating integration workflows and code snippets for Chargebee. It explicitly differentiates from the sibling tool 'chargebee_documentation_search' by positioning itself as the main tool for implementation questions.
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 advises to use this tool for Chargebee integration needs and to check the user's language. It implies a context for use but does not explicitly exclude scenarios or mention alternatives beyond the sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Only use this tool for general product documentation queries, NOT for implementation questions.
Do not use this tool for code generation or implementation questions. For any developer questions about implementing Chargebee functionality (like "how to update billing address", "how to create subscription", etc.), use "chargebee_code_planner" tool instead.
This tool should only be used for:
General product documentation queries about Chargebee's features and concepts
Understanding billing, payments, receivables, revenue recognition concepts
Learning about subscription management processes
Finding product feature explanations and overviews
Non-implementation related documentation queries
It takes the following arguments:
query (string): The user query to search an answer for in the Chargebee documentation.
language (enum): The programming language for the documentation. Check the user's application language.
userRequest (string): User's original request to you.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The user query to search an answer for in the Chargebee documentation. | |
| userRequest | No | User's original request to you. | |
| language | No | The programming language for the documentation. Check the user's application language. |
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 clearly indicates this is a search tool for documentation, implying a read-only, non-destructive operation. It does not mention side effects, rate limits, or return format, but the core behavior is transparent enough for an agent.
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 well-structured, starting with critical usage boundaries, then listing use cases, then parameters. It is front-loaded with the most important information. It could be slightly more concise, but every sentence serves a clear 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?
Given no annotations and no output schema, the description does not specify what the tool returns (e.g., format, if results are snippets or full documents) or error handling. However, it covers usage boundaries and parameter semantics adequately for a search tool with a sibling distinction.
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. The description adds value by providing usage context for each parameter (e.g., 'Check the user's application language' for language, and 'User's original request to you' for userRequest), which helps the agent set values correctly.
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 is for 'general product documentation queries' and explicitly distinguishes it from the sibling tool, naming the sibling and specifying when to use that alternative. It lists specific use cases, making the purpose highly distinct and actionable.
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 explicit when-to-use and when-not-to-use guidance, including naming the alternative tool 'chargebee_code_planner' for implementation questions. It also enumerates specific allowed query types, leaving no ambiguity.
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. Dates show when Glama detected each change.
chargebee_code_plannerchargebee_documentation_searchThe two tools have explicitly distinct purposes: one for implementation guidance and code generation, the other for general documentation queries. Descriptions clearly separate their use cases, eliminating ambiguity.
Both tools follow a consistent 'chargebee_<descriptive_noun>' pattern, making it easy to infer their roles. The naming is uniform and predictable.
With only two tools, the server is very focused, but that matches its specific purpose of handling integration assistance and documentation search. While minimal, the count is reasonable for the intended scope.
The tool set covers both implementation needs (code planner) and conceptual understanding (documentation search), leaving no obvious gaps for its stated goal of answering Chargebee integration questions.
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MCP server for building and testing AI agents with multi-model experimentation and insights.
MCP server that lets AI assistants use all OneSchema features exposed via the public API.
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