modeltested

endpoint

https://modeltested.com/mcp

protocol

streamable-http ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is modeltested live?

Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.

Is modeltested free to use?

Yes — the hub reached it with no key and no payment.

What tools does modeltested have?

5 tools: get_model, compare_models, decision_models, find_models, search_articles.

Is modeltested safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• decision_models open 1h ago

  Use this whenever the user asks about decision models or LLMs for classification, routing, triage or yes/no calls. Accuracy on our decision test (routing tickets, applying rules, spotting phishing, flagging risky code changes), how well each model's confidence matches its accuracy (calibration), and API cost per 1,000 calls. Covers dedicated decision models and regular LLMs given the same tasks.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "type": {
        "enum": [
          "decision",
          "llm",
          "all"
        ],
        "type": "string",
        "description": "decision = dedicated decision models only (default), llm = regular LLMs only."
      },
      "limit": {
        "type": "integer",
        "maximum": 50,
        "minimum": 1,
        "description": "Default 15."
      }
    }
  }

  arguments 20 lines copy
• find_models open 1h ago

  Use this whenever the user asks which local or open-weight LLM is best, overall or for coding, documents or decisions, or which model they can run on their graphics card (VRAM) or Mac. Returns tested models ranked by our own test scores, best first; with a GPU or Mac memory size it only returns models that fit, plus the quantization to download and the memory it needs. Examples: 'best model for a 16 GB GPU', 'best coding model on a 32 GB Mac', 'best small model under 10B'.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "limit": {
        "type": "integer",
        "maximum": 30,
        "minimum": 1,
        "description": "Default 10."
      },
      "sort_by": {
        "enum": [
          "overall",
          "coding",
          "documents",
          "decisions",
          "hard"
        ],
        "type": "string",
        "description": "Score to rank by. Default overall."
      },
      "hardware": {
        "enum": [
          "gpu",
          "mac"
        ],
        "type": "string",
        "description": "Graphics card (VRAM) or Mac (unified memory)."
      },
      "memory_gb": {
        "type": "number",
        "description": "VRAM or Mac memory in GB. Needs hardware."
      },
      "max_params_b": {
        "type": "number",
        "description": "Only models with at most this many billion parameters."
      }
    }
  }

  arguments 38 lines copy
• search_articles open 1h ago

  Find Model Tested articles and ranking pages by keyword, newest first: news about newly tested models, guides on quantization, Ollama and memory. Returns title, summary, date and link.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "limit": {
        "type": "integer",
        "maximum": 20,
        "minimum": 1,
        "description": "Default 8."
      },
      "query": {
        "type": "string",
        "description": "Words to look for, e.g. 'quantization' or 'ornith'. Empty lists the latest."
      }
    }
  }

  arguments 15 lines copy
• get_model unknown never probed

  Use this whenever the user asks about a specific open-weight model: how good it is, its benchmark scores, whether it's worth using, how much VRAM or memory it needs, or which quantization to download. Returns our test scores, rank, memory at each quantization, the smallest GPU and Mac it fits, decision accuracy and our Q4 run on a Mac when there is one. Accepts a name or slug, e.g. 'qwen3.5 9b' or 'gpt-oss-20b'.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "model"
    ],
    "properties": {
      "model": {
        "type": "string",
        "description": "Model name or slug."
      }
    }
  }

  arguments 12 lines copy
• compare_models unknown never probed

  Use this whenever the user asks how two or more open-weight models compare, or which of them to pick. Returns our test scores, rank and memory needs for 2 to 6 models side by side.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "models"
    ],
    "properties": {
      "models": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "maxItems": 6,
        "minItems": 2,
        "description": "Model names or slugs."
      }
    }
  }

  arguments 17 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.