Un servidor de Protocolo de Contexto de Modelo (MCP) basado en TypeScript que facilita el diálogo inteligente sobre características entre desarrolladores e IA. Este servidor actúa como desarrollador líder de IA, proporcionando orientación sobre la implementación de características, manteniendo el contexto de las discusiones y ayudando a los equipos a tomar decisiones arquitectónicas informadas.
Este servidor proporciona:
Discusiones interactivas sobre la implementación y arquitectura de funciones
Memoria persistente de las discusiones y decisiones sobre las características
Orientación inteligente sobre enfoques de desarrollo y mejores prácticas
Recomendaciones contextuales basadas en el historial del proyecto
Participe en debates naturales sobre los requisitos de las funciones
Obtenga orientación experta sobre enfoques de implementación
Recibir recomendaciones arquitectónicas
Mantener el contexto en múltiples discusiones
Almacenamiento persistente de debates sobre funciones
Seguimiento de la evolución de las características y decisiones
Consulte discusiones anteriores para obtener contexto.
Funciones y dependencias relacionadas con los enlaces
Recomendaciones de mejores prácticas
Sugerencias de estrategia de implementación
Recomendaciones de patrones de arquitectura
Consideraciones sobre la pila de tecnología
Mantener el contexto de las características de todo el proyecto
Realizar un seguimiento de las dependencias entre funciones
Decisiones arquitectónicas de la tienda
Recuerde los resultados de discusiones anteriores
Related MCP server: Context Management System
Para utilizar con Claude Desktop, agregue la configuración del servidor:
En MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json En Windows: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"feature-discussion": {
"command": "/path/to/feature-discussion/build/index.js"
}
}
}Instalar dependencias:
npm installConstruir el servidor:
npm run buildPara desarrollo con reconstrucción automática:
npm run watchDado que los servidores MCP se comunican a través de stdio, la depuración puede ser complicada. Recomendamos usar el Inspector MCP , disponible como script de paquete:
npm run inspectorEl Inspector proporcionará una URL para acceder a las herramientas de depuración en su navegador.
¡Agradecemos tus contribuciones! Consulta nuestras Pautas de Contribución para saber cómo empezar y nuestro Código de Conducta para conocer las normas de la comunidad.
Este proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.
Start a new feature discussion
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Title or name of the feature |
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. 'Start a new feature discussion' implies a creation/mutation operation, but it doesn't specify permissions needed, whether this is reversible, what happens after starting, or any rate limits. It lacks crucial context for safe and effective use.
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 appropriately sized for a simple tool and front-loads the core purpose 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 a mutation tool with behavioral gaps, the description is incomplete. It doesn't explain what 'starting a feature discussion' entails, what the result looks like, or how it relates to the sibling tool. More context is needed for effective 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 100%, so the schema already documents the single 'title' parameter. The description adds no additional meaning about parameters beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.
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 'Start a new feature discussion' clearly indicates the action (start) and resource (feature discussion), but it's somewhat vague about what this entails. It doesn't specify what a 'feature discussion' is in this context or how it differs from the sibling tool 'provide_feature_input'.
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 the sibling 'provide_feature_input'. There's no mention of prerequisites, context, or alternatives. The user must 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.
Provide information for the current feature discussion prompt
| Name | Required | Description | Default |
|---|---|---|---|
| featureId | Yes | ID of the feature being discussed | |
| response | Yes | Your response to the current prompt |
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 'provide information', which suggests a read or input operation, but doesn't clarify if this is a mutation, requires specific permissions, has side effects, or details the response 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized for its function, making it highly concise 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?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns, how it interacts with the sibling tool 'begin_feature_discussion', or provide behavioral details. For a tool with two required parameters and no structured support, the description should offer more context to guide the agent effectively.
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 description coverage is 100%, with clear descriptions for both parameters ('featureId' and 'response'). The description adds no additional meaning beyond what the schema provides, such as explaining the context or format of 'response'. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter documentation adequately.
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's purpose as 'Provide information for the current feature discussion prompt', which is clear but vague. It specifies the action ('provide information') and context ('current feature discussion prompt'), but doesn't clarify what type of information or how it differs from the sibling tool 'begin_feature_discussion'. This makes it adequate but with room for improvement.
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, such as the sibling tool 'begin_feature_discussion'. It implies usage in the context of a 'current feature discussion prompt', but lacks explicit instructions on prerequisites, timing, or comparisons to other tools, leaving the agent with minimal direction.
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.
begin_feature_discussionprovide_feature_inputScored across 2 tools
The two tools have clearly distinct purposes: 'begin_feature_discussion' initiates a new discussion, while 'provide_feature_input' adds information to an existing one. There is no overlap in functionality, making it impossible for an agent to confuse them.
Both tools follow a consistent verb_noun pattern with snake_case naming. 'begin_feature_discussion' and 'provide_feature_input' use clear, descriptive verbs ('begin' and 'provide') paired with the same noun phrase ('feature_discussion' or 'feature_input'), ensuring predictability.
With only 2 tools, the server feels thin for a feature discussion domain. It lacks essential operations like retrieving, updating, or closing discussions, which limits its utility and suggests an incomplete scope.
The tool surface is severely incomplete for feature discussions. It covers starting a discussion and providing input, but missing critical operations such as getting discussion details, listing discussions, updating status, or adding comments, which will likely cause agent failures in real workflows.
AI colleagues that keep your standards, your project and their reasoning between sessions
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