LLM Bridge MCP permite a los agentes de IA interactuar con múltiples modelos de lenguaje extensos a través de una interfaz estandarizada. Utiliza el Protocolo de Control de Mensajes (MCP) para proporcionar un acceso fluido a diferentes proveedores de LLM, lo que facilita el cambio entre modelos o el uso de varios en la misma aplicación.
Interfaz unificada para múltiples proveedores de LLM:
OpenAI (modelos GPT)
Antrópico (modelos de Claude)
Google (modelos Gemini)
Búsqueda profunda
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Creado con Pydantic AI para seguridad y validación de tipos
Admite parámetros personalizables como temperatura y tokens máximos.
Proporciona seguimiento de uso y métricas.
Related MCP server: MindBridge MCP Server
El servidor implementa la siguiente herramienta:
run_llm(
prompt: str,
model_name: KnownModelName = "openai:gpt-4o-mini",
temperature: float = 0.7,
max_tokens: int = 8192,
system_prompt: str = "",
) -> LLMResponseprompt : El mensaje de texto que se enviará al LLM
model_name : Modelo específico a utilizar (predeterminado: "openai:gpt-4o-mini")
temperature : controla la aleatoriedad (0,0 a 1,0)
max_tokens : Número máximo de tokens a generar
system_prompt : mensaje del sistema opcional para guiar el comportamiento del modelo
Para instalar llm-bridge-mcp para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install @sjquant/llm-bridge-mcp --client claudeClonar el repositorio:
git clone https://github.com/yourusername/llm-bridge-mcp.git
cd llm-bridge-mcpInstalar uv (si aún no está instalado):
# On macOS
brew install uv
# On Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# On Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"Cree un archivo .env en el directorio raíz con sus claves API:
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key
GOOGLE_API_KEY=your_google_api_key
DEEPSEEK_API_KEY=your_deepseek_api_keyAgregue una entrada de servidor a su archivo de configuración de Claude Desktop o .cursor/mcp.json :
"mcpServers": {
"llm-bridge": {
"command": "uvx",
"args": [
"llm-bridge-mcp"
],
"env": {
"OPENAI_API_KEY": "your_openai_api_key",
"ANTHROPIC_API_KEY": "your_anthropic_api_key",
"GOOGLE_API_KEY": "your_google_api_key",
"DEEPSEEK_API_KEY": "your_deepseek_api_key"
}
}
}Este error ocurre cuando el sistema no puede encontrar el ejecutable uvx en su PATH. Para solucionarlo:
Solución: utilice la ruta completa a uvx
Encuentre la ruta completa a su ejecutable uvx:
# On macOS/Linux
which uvx
# On Windows
where.exe uvxLuego actualice la configuración de su servidor MCP para utilizar la ruta completa:
"mcpServers": {
"llm-bridge": {
"command": "/full/path/to/uvx", // Replace with your actual path
"args": [
"llm-bridge-mcp"
],
"env": {
// ... your environment variables
}
}
}Este proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.
Run a prompt through an LLM and return the response.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | ||
| model_name | No | Specific model name. Available models: anthropic:claude-3-7-sonnet-latest, anthropic:claude-3-5-haiku-latest, anthropic:claude-3-5-sonnet-latest, anthropic:claude-3-opus-latest, claude-3-7-sonnet-latest, claude-3-5-haiku-latest, bedrock:amazon.titan-tg1-large, bedrock:amazon.titan-text-lite-v1, bedrock:amazon.titan-text-express-v1, bedrock:us.amazon.nova-pro-v1:0, bedrock:us.amazon.nova-lite-v1:0, bedrock:us.amazon.nova-micro-v1:0, bedrock:anthropic.claude-3-5-sonnet-20241022-v2:0, bedrock:us.anthropic.claude-3-5-sonnet-20241022-v2:0, bedrock:anthropic.claude-3-5-haiku-20241022-v1:0, bedrock:us.anthropic.claude-3-5-haiku-20241022-v1:0, bedrock:anthropic.claude-instant-v1, bedrock:anthropic.claude-v2:1, bedrock:anthropic.claude-v2, bedrock:anthropic.claude-3-sonnet-20240229-v1:0, bedrock:us.anthropic.claude-3-sonnet-20240229-v1:0, bedrock:anthropic.claude-3-haiku-20240307-v1:0, bedrock:us.anthropic.claude-3-haiku-20240307-v1:0, bedrock:anthropic.claude-3-opus-20240229-v1:0, bedrock:us.anthropic.claude-3-opus-20240229-v1:0, bedrock:anthropic.claude-3-5-sonnet-20240620-v1:0, bedrock:us.anthropic.claude-3-5-sonnet-20240620-v1:0, bedrock:anthropic.claude-3-7-sonnet-20250219-v1:0, bedrock:us.anthropic.claude-3-7-sonnet-20250219-v1:0, bedrock:cohere.command-text-v14, bedrock:cohere.command-r-v1:0, bedrock:cohere.command-r-plus-v1:0, bedrock:cohere.command-light-text-v14, bedrock:meta.llama3-8b-instruct-v1:0, bedrock:meta.llama3-70b-instruct-v1:0, bedrock:meta.llama3-1-8b-instruct-v1:0, bedrock:us.meta.llama3-1-8b-instruct-v1:0, bedrock:meta.llama3-1-70b-instruct-v1:0, bedrock:us.meta.llama3-1-70b-instruct-v1:0, bedrock:meta.llama3-1-405b-instruct-v1:0, bedrock:us.meta.llama3-2-11b-instruct-v1:0, bedrock:us.meta.llama3-2-90b-instruct-v1:0, bedrock:us.meta.llama3-2-1b-instruct-v1:0, bedrock:us.meta.llama3-2-3b-instruct-v1:0, bedrock:us.meta.llama3-3-70b-instruct-v1:0, bedrock:mistral.mistral-7b-instruct-v0:2, bedrock:mistral.mixtral-8x7b-instruct-v0:1, bedrock:mistral.mistral-large-2402-v1:0, bedrock:mistral.mistral-large-2407-v1:0, claude-3-5-sonnet-latest, claude-3-opus-latest, cohere:c4ai-aya-expanse-32b, cohere:c4ai-aya-expanse-8b, cohere:command, cohere:command-light, cohere:command-light-nightly, cohere:command-nightly, cohere:command-r, cohere:command-r-03-2024, cohere:command-r-08-2024, cohere:command-r-plus, cohere:command-r-plus-04-2024, cohere:command-r-plus-08-2024, cohere:command-r7b-12-2024, deepseek:deepseek-chat, deepseek:deepseek-reasoner, google-gla:gemini-1.0-pro, google-gla:gemini-1.5-flash, google-gla:gemini-1.5-flash-8b, google-gla:gemini-1.5-pro, google-gla:gemini-2.0-flash-exp, google-gla:gemini-2.0-flash-thinking-exp-01-21, google-gla:gemini-exp-1206, google-gla:gemini-2.0-flash, google-gla:gemini-2.0-flash-lite-preview-02-05, google-gla:gemini-2.0-pro-exp-02-05, google-vertex:gemini-1.0-pro, google-vertex:gemini-1.5-flash, google-vertex:gemini-1.5-flash-8b, google-vertex:gemini-1.5-pro, google-vertex:gemini-2.0-flash-exp, google-vertex:gemini-2.0-flash-thinking-exp-01-21, google-vertex:gemini-exp-1206, google-vertex:gemini-2.0-flash, google-vertex:gemini-2.0-flash-lite-preview-02-05, google-vertex:gemini-2.0-pro-exp-02-05, gpt-3.5-turbo, gpt-3.5-turbo-0125, gpt-3.5-turbo-0301, gpt-3.5-turbo-0613, gpt-3.5-turbo-1106, gpt-3.5-turbo-16k, gpt-3.5-turbo-16k-0613, gpt-4, gpt-4-0125-preview, gpt-4-0314, gpt-4-0613, gpt-4-1106-preview, gpt-4-32k, gpt-4-32k-0314, gpt-4-32k-0613, gpt-4-turbo, gpt-4-turbo-2024-04-09, gpt-4-turbo-preview, gpt-4-vision-preview, gpt-4.5-preview, gpt-4.5-preview-2025-02-27, gpt-4o, gpt-4o-2024-05-13, gpt-4o-2024-08-06, gpt-4o-2024-11-20, gpt-4o-audio-preview, gpt-4o-audio-preview-2024-10-01, gpt-4o-audio-preview-2024-12-17, gpt-4o-mini, gpt-4o-mini-2024-07-18, gpt-4o-mini-audio-preview, gpt-4o-mini-audio-preview-2024-12-17, groq:gemma2-9b-it, groq:llama-3.1-8b-instant, groq:llama-3.2-11b-vision-preview, groq:llama-3.2-1b-preview, groq:llama-3.2-3b-preview, groq:llama-3.2-90b-vision-preview, groq:llama-3.3-70b-specdec, groq:llama-3.3-70b-versatile, groq:llama3-70b-8192, groq:llama3-8b-8192, groq:mixtral-8x7b-32768, mistral:codestral-latest, mistral:mistral-large-latest, mistral:mistral-moderation-latest, mistral:mistral-small-latest, o1, o1-2024-12-17, o1-mini, o1-mini-2024-09-12, o1-preview, o1-preview-2024-09-12, o3-mini, o3-mini-2025-01-31, openai:chatgpt-4o-latest, openai:gpt-3.5-turbo, openai:gpt-3.5-turbo-0125, openai:gpt-3.5-turbo-0301, openai:gpt-3.5-turbo-0613, openai:gpt-3.5-turbo-1106, openai:gpt-3.5-turbo-16k, openai:gpt-3.5-turbo-16k-0613, openai:gpt-4, openai:gpt-4-0125-preview, openai:gpt-4-0314, openai:gpt-4-0613, openai:gpt-4-1106-preview, openai:gpt-4-32k, openai:gpt-4-32k-0314, openai:gpt-4-32k-0613, openai:gpt-4-turbo, openai:gpt-4-turbo-2024-04-09, openai:gpt-4-turbo-preview, openai:gpt-4-vision-preview, openai:gpt-4.5-preview, openai:gpt-4.5-preview-2025-02-27, openai:gpt-4o, openai:gpt-4o-2024-05-13, openai:gpt-4o-2024-08-06, openai:gpt-4o-2024-11-20, openai:gpt-4o-audio-preview, openai:gpt-4o-audio-preview-2024-10-01, openai:gpt-4o-audio-preview-2024-12-17, openai:gpt-4o-mini, openai:gpt-4o-mini-2024-07-18, openai:gpt-4o-mini-audio-preview, openai:gpt-4o-mini-audio-preview-2024-12-17, openai:o1, openai:o1-2024-12-17, openai:o1-mini, openai:o1-mini-2024-09-12, openai:o1-preview, openai:o1-preview-2024-09-12, openai:o3-mini, openai:o3-mini-2025-01-31, test | openai:gpt-4o-mini |
| temperature | No | Controls randomness (0.0 to 1.0) | |
| max_tokens | No | Maximum number of tokens to generate | |
| system_prompt | No | Optional system prompt to guide the model's behavior |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral transparency. It fails to mention important traits like streaming, cost, latency, or error handling, leaving the agent uninformed.
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 one concise sentence, front-loading the action. It is not verbose, but it omits critical details, so it is not a perfect 5.
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?
With 5 parameters, no output schema, and no annotations, the description is incomplete. It does not describe return values, optional parameters, or implications of choices like model or temperature.
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 80%, so the burden on the description is lower. However, the description adds no value beyond the schema—it does not explain any parameter semantics, so a baseline of 3 is appropriate.
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 action ('run a prompt') and the resource ('LLM'), with the purpose of getting a response. It is specific and not a tautology.
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. Since there are no sibling tools, the lack is less critical, but the description still offers no usage context or preconditions.
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.
v1.0.0run_llmScored across 1 tool
With only one tool, there is no possibility of ambiguity. The tool's purpose is clear and distinct.
A single tool means naming is trivially consistent. 'run_llm' is descriptive and follows a clear verb_noun pattern.
One tool for an LLM bridge is far too few. Typical servers in this domain include multiple tools for model selection, streaming, or context management.
The server offers only a basic 'run' operation with no supporting tools for model listing, configuration, or advanced features, leaving significant gaps.
One MCP endpoint for Claude, GPT & Gemini: 100+ tools + no-code connectors + agent workers.
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