Envíe solicitudes a OpenAI, MistralAI, Anthropic, xAI, Google AI, DeepSeek, Alibaba e Inception mediante el protocolo MCP mediante una herramienta o indicaciones predefinidas. Se requiere la clave API del proveedor.
El servidor implementa una herramienta:
unichat : Enviar una solicitud a unichat
Toma "mensajes" como argumentos de cadena requeridos
Devuelve una respuesta
code_review
Revisar el código para conocer las mejores prácticas, posibles problemas y mejoras.
Argumentos:
code (cadena, obligatorio): El código a revisar"
document_code
Generar documentación para el código, incluidas cadenas de documentación y comentarios.
Argumentos:
code (cadena, obligatorio): El código a comentar"
explain_code
Explicar cómo funciona un fragmento de código en detalle.
Argumentos:
code (cadena, obligatorio): El código para explicar"
code_rework
Aplicar los cambios solicitados al código proporcionado
Argumentos:
changes (cadena, opcional): Los cambios a aplicar"
code (cadena, obligatorio): El código que se va a reelaborar"
Related MCP server: MCP AI Gateway
En MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json En Windows: %APPDATA%/Claude/claude_desktop_config.json
Modelos compatibles:
Puede encontrar una lista de los modelos compatibles actualmente que se pueden usar como
"SELECTED_UNICHAT_MODEL"aquí . Asegúrese de agregar la clave API del proveedor correspondiente como"YOUR_UNICHAT_API_KEY"
Ejemplo:
"env": {
"UNICHAT_MODEL": "gpt-4o-mini",
"UNICHAT_API_KEY": "YOUR_OPENAI_API_KEY"
}Configuración de servidores no publicados/desarrollo
"mcpServers": {
"unichat-mcp-server": {
"command": "uv",
"args": [
"--directory",
"{{your source code local directory}}/unichat-mcp-server",
"run",
"unichat-mcp-server"
],
"env": {
"UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
"UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
}
}
}Configuración de servidores publicados
"mcpServers": {
"unichat-mcp-server": {
"command": "uvx",
"args": [
"unichat-mcp-server"
],
"env": {
"UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
"UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
}
}
}Para instalar Unichat para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install unichat-mcp-server --client claudePara preparar el paquete para su distribución:
Eliminar compilaciones antiguas:
rm -rf distSincronizar dependencias y actualizar archivo de bloqueo:
uv syncDistribuciones de paquetes de compilación:
uv buildEsto creará distribuciones de origen y de rueda en el directorio dist/ .
Publicar en PyPI:
uv publish --token {{YOUR_PYPI_API_TOKEN}}Dado que los servidores MCP se ejecutan en stdio, la depuración puede ser complicada. Para una experiencia óptima, recomendamos usar el Inspector MCP .
Puede iniciar el Inspector MCP a través de npm con este comando:
npx @modelcontextprotocol/inspector uv --directory {{your source code local directory}}/unichat-mcp-server run unichat-mcp-serverAl iniciarse, el Inspector mostrará una URL a la que podrá acceder en su navegador para comenzar a depurar.
Chat with an assistant. Example tool use message: Ask the unichat to review and evaluate your proposal.
| Name | Required | Description | Default |
|---|---|---|---|
| messages | Yes | Array of exactly two messages: first a system message defining the task, then a user message with the specific query |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions nothing about behavioral traits like whether this is a read-only operation, if it requires authentication, rate limits, or what kind of responses to expect. The example hints at evaluation tasks but doesn't disclose operational characteristics.
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 brief but includes an example that adds some value. However, the formatting with extra whitespace is awkward, and the example could be integrated more cleanly. It's not excessively verbose, but the structure could be improved for better front-loading of information.
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 chat tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the assistant does, what domains it covers, what format responses take, or any limitations. The example provides minimal context but doesn't compensate for the lack of structured information about this interactive tool.
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 fully documents the single parameter (messages array with exactly two messages). The description adds no parameter information beyond what's in the schema, not even mentioning the two-message requirement. Baseline 3 is appropriate when schema does all the work.
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 'Chat with an assistant' which indicates the basic function, but it's vague about what this assistant does or what domain it operates in. The example tool use message adds some context about reviewing proposals, but doesn't make the purpose specific or distinguish it from other chat tools. It's not tautological but lacks clear differentiation.
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 example suggests it can be used for reviewing proposals, but there's no mention of prerequisites, limitations, or when not to use it. With no sibling tools, the bar is lower, but still lacks basic usage context.
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.
unichatScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'unichat' has a clear and distinct purpose of chatting with an assistant.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'unichat' follows a simple, readable pattern without any conflicting conventions.
A single tool is too few for most server purposes, as it severely limits functionality and scope. While it might be appropriate for a minimal chat interface, it feels thin and lacks the depth expected for a typical MCP server, which usually requires multiple tools to handle different operations or resources.
For a chat assistant domain, the single tool 'unichat' covers the core action of chatting, but there are notable gaps. It lacks operations for managing chat history, configuring settings, or handling multiple sessions, which are common in chat systems. However, the basic functionality is present, allowing agents to perform the primary task.
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