LLM MCP Bridge
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LLM_API_KEY | No | API Key (requerida para servicios cloud) | |
| LLM_BASE_URL | No | URL del servidor LLM | http://localhost:1234/v1 |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| llm_get_modelsC | Obtiene la lista de modelos disponibles en el servidor LLM (compatible con OpenAI API: LM Studio, Ollama, vLLM, OpenAI, etc.) |
| llm_statusB | Verifica el estado de conexión con el servidor LLM y lista los modelos disponibles |
| llm_chatC | Envía un prompt al modelo y recibe una respuesta con métricas de rendimiento (latencia, tokens/s) |
| llm_benchmarkC | Ejecuta un benchmark con múltiples prompts para evaluar rendimiento del modelo |
| llm_evaluate_coherenceC | Evalúa la coherencia del modelo ejecutando el mismo prompt múltiples veces |
| llm_test_capabilitiesC | Prueba las capacidades del modelo en diferentes áreas: razonamiento, código, creatividad, hechos, instrucciones |
| llm_compare_modelsC | Compara el rendimiento de múltiples modelos con el mismo prompt |
| llm_quality_reportC | Genera un reporte completo de calidad del modelo incluyendo benchmark, coherencia y capacidades |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 8 tools
Most tools have distinct purposes like benchmarking, chatting, and comparing models, but there is overlap between llm_get_models and llm_status, as both list available models. The descriptions help differentiate them, but an agent might still be confused about which to use for model listing.
All tool names follow a consistent 'llm_' prefix with descriptive suffixes in snake_case, such as llm_benchmark and llm_chat. This predictable pattern makes it easy for agents to understand and navigate the toolset without confusion.
With 8 tools, the count is well-scoped for evaluating and testing LLM models. Each tool serves a specific function in performance analysis, quality assessment, and model management, fitting the server's purpose without being overwhelming or insufficient.
The toolset covers key aspects of LLM evaluation, including benchmarking, chatting, model comparison, coherence testing, and capability assessment. A minor gap is the lack of tools for model configuration or fine-tuning, but core workflows are well-covered for quality and performance analysis.