SoftPerceptron
AI model/agent directory with pricing and comparisons, plus GPU VRAM fit and tokens/sec estimates.
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Calidad y seguridad
Hallazgos (1)
- LOWTool 'compare_ai_models' description lacks action verben compare_ai_models
Basado en el análisis automatizado de las definiciones de herramientas y el cumplimiento del protocolo.
Costo de contexto
Este es el número aproximado de tokens que se consumen cada vez que las herramientas del servidor se cargan en el contexto de un modelo. Los recuentos más altos reducen la atención disponible para otras tareas.
Instalar
Instalación con un clic
Agrega esto a tu archivo `claude_desktop_config.json`:
{
"mcpServers": {
"mcp": {
"url": "https://softperceptron.com/mcp"
}
}
}Puntos de conexión remotos
https://softperceptron.com/mcpstreamable-httpQué puede hacer
Inventario de herramientas
Herramientas (6)
🟢search_ai_models(query, kind, limit)
Search the SoftPerceptron directory of AI models, autonomous agents and token/credit products. Returns pricing, specs and provider for each match. Use this to answer questions like 'what AI models are available', 'cheapest model with a big context window', or 'which coding agents exist'.
Esquema de entrada
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Free-text match against name, tagline, provider and tags. Omit to list everything."
},
"kind": {
"type": "string",
"enum": [
"model",
"agent",
"token"
],
"description": "Restrict to one product kind."
},
"limit": {
"type": "integer",
"minimum": 1,
"maximum": 50,
"description": "Max results (default 10)."
}
}
}🟢get_ai_model(id)
Get full details for one directory entry by id or common alias (e.g. 'claude', 'gpt', 'gemini'). Returns pricing, specs, provider and links.
Esquema de entrada
{
"type": "object",
"properties": {
"id": {
"type": "string",
"description": "Listing id or alias, e.g. 'anthropic-claude' or 'claude'."
}
},
"required": [
"id"
]
}⚪compare_ai_models(a, b)
Compare two directory entries side by side — pricing, context window, modality and what each is best for. Both must be the same kind (model vs model, agent vs agent).
Esquema de entrada
{
"type": "object",
"properties": {
"a": {
"type": "string",
"description": "First listing id or alias."
},
"b": {
"type": "string",
"description": "Second listing id or alias."
}
},
"required": [
"a",
"b"
]
}🟢can_i_run_model(model, gpu, quant, context)
Check whether a specific open-weight model fits on a specific GPU: VRAM required, whether it fits, how much headroom, which quantization to use, and an estimated decode speed in tokens/sec. Use for questions like 'can an RTX 4090 run Llama 70B'.
Esquema de entrada
{
"type": "object",
"properties": {
"model": {
"type": "string",
"description": "Local model id or name, e.g. 'llama33-70b'. Call list_local_models to see options."
},
"gpu": {
"type": "string",
"description": "GPU id or name, e.g. 'rtx4090'. Call list_gpus to see options."
},
"quant": {
"type": "string",
"description": "Quantization id (default 'q4'), e.g. q4, q5, q8, fp16."
},
"context": {
"type": "integer",
"minimum": 512,
"maximum": 200000,
"description": "Context length in tokens (default 8192)."
}
},
"required": [
"model",
"gpu"
]
}🟢list_local_models
List the open-weight models supported by the VRAM/can-i-run calculator, with parameter counts and what each is good for.
Esquema de entrada
{
"type": "object",
"properties": {}
}🟢list_gpus
List the GPUs supported by the VRAM calculator, with VRAM, memory bandwidth and power draw.
Esquema de entrada
{
"type": "object",
"properties": {}
}Comunidad
Evidencia