warpgbm-mcp

endpoint

https://warpgbm.ai/mcp/sse

protocol

streamable-http ·2024-11-05

authentication

none observed

public key

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

karma

0 · newcomer

Is warpgbm-mcp live?

Yes — it answered the hub's last check (checked 1h ago). It answered 97% of checks over the last 30 days.

Is warpgbm-mcp free to use?

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

What tools does warpgbm-mcp have?

6 tools: train, predict_from_artifact, upload_data, submit_feedback, get_agent_guide, list_models.

Is warpgbm-mcp 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.

• get_agent_guide reads open 23h ago

  Get the comprehensive agent guide with examples, best practices, and troubleshooting tips for using this service

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• list_models reads open 23h ago

  List all available ML model backends (warpgbm, lightgbm)

  mcp-tool

  1234

  {
    "type": "object",
    "properties": {}
  }

  arguments 4 lines copy
• train changes data unknown never probed

  Train a gradient boosting model and return portable artifacts (joblib and/or ONNX)

  mcp-tool

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  {
    "type": "object",
    "required": [
      "X",
      "y"
    ],
    "properties": {
      "X": {
        "type": "array",
        "description": "Feature matrix (2D array of floats)"
      },
      "y": {
        "type": "array",
        "description": "Target labels (floats for regression, integers for classification)"
      },
      "max_depth": {
        "type": "integer",
        "default": 6,
        "description": "Maximum tree depth"
      },
      "objective": {
        "enum": [
          "regression",
          "binary",
          "multiclass"
        ],
        "type": "string",
        "default": "multiclass",
        "description": "Training objective"
      },
      "model_type": {
        "enum": [
          "warpgbm",
          "lightgbm"
        ],
        "type": "string",
        "default": "warpgbm",
        "description": "Model backend to use"
      },
      "n_estimators": {
        "type": "integer",
        "default": 100,
        "description": "Number of trees"
      },
      "learning_rate": {
        "type": "number",
        "default": 0.1,
        "description": "Learning rate"
      }
    }
  }

  arguments 51 lines copy
• predict_from_artifact reads unknown never probed

  Run inference using a trained model artifact or artifact_id. Use artifact_id for fast predictions right after training (valid for 5 minutes).

  mcp-tool

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  {
    "type": "object",
    "required": [
      "X"
    ],
    "properties": {
      "X": {
        "type": "array",
        "description": "Feature matrix for prediction (2D array)"
      },
      "artifact_id": {
        "type": "string",
        "description": "Temporary artifact ID from training response (fast, valid for 5 minutes)"
      },
      "model_artifact_joblib": {
        "type": "string",
        "description": "Base64-encoded, gzip-compressed joblib model"
      }
    }
  }

  arguments 20 lines copy
• upload_data changes data unknown never probed

  Upload CSV or Parquet files for training. Parses files and returns structured X and y arrays ready for training.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "file_content",
      "file_format"
    ],
    "properties": {
      "file_format": {
        "enum": [
          "csv",
          "parquet"
        ],
        "type": "string",
        "description": "File format"
      },
      "file_content": {
        "type": "string",
        "description": "Base64-encoded file content"
      },
      "target_column": {
        "type": "string",
        "description": "Column name for target variable (y)"
      },
      "feature_columns": {
        "type": "array",
        "description": "Column names for features (X). If not specified, all columns except target are used."
      }
    }
  }

  arguments 29 lines copy
• submit_feedback changes data unknown never probed

  Submit feedback about the service. Agents can report bugs, request features, or provide general feedback.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "feedback_type",
      "message"
    ],
    "properties": {
      "message": {
        "type": "string",
        "description": "Feedback message"
      },
      "endpoint": {
        "type": "string",
        "description": "Related endpoint (if applicable)"
      },
      "severity": {
        "enum": [
          "low",
          "medium",
          "high",
          "critical"
        ],
        "type": "string",
        "default": "medium",
        "description": "Severity level"
      },
      "agent_info": {
        "type": "object",
        "description": "Agent metadata (name, version, etc.)"
      },
      "model_type": {
        "type": "string",
        "description": "Related model type (if applicable)"
      },
      "feedback_type": {
        "enum": [
          "bug",
          "feature_request",
          "documentation",
          "performance",
          "general"
        ],
        "type": "string",
        "description": "Type of feedback"
      }
    }
  }

  arguments 47 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.