SoftPerceptron
AI model/agent directory with pricing and comparisons, plus GPU VRAM fit and tokens/sec estimates.
Should I use this
Quality & Safety
Findings (1)
- LOWTool 'compare_ai_models' description lacks action verbin compare_ai_models
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"mcp": {
"url": "https://softperceptron.com/mcp"
}
}
}Remote endpoints
https://softperceptron.com/mcpstreamable-httpWhat it can do
Tool inventory
Tools (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'.
Input Schema
{
"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.
Input Schema
{
"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).
Input Schema
{
"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'.
Input Schema
{
"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.
Input Schema
{
"type": "object",
"properties": {}
}🟢list_gpus
List the GPUs supported by the VRAM calculator, with VRAM, memory bandwidth and power draw.
Input Schema
{
"type": "object",
"properties": {}
}Community
Evidence