Tools Overview

Foggy MCP provides a series of data query and analysis tools for AI assistants to call.

Tool List

Tool	Description	Category
dataset.get_metadata	Get user-accessible model list	Metadata
dataset.describe_model_internal	Get model field details	Metadata
dataset.query_model	Execute structured data query	Query
dataset_nl.query	Natural language data query	Natural Language
chart.generate	Generate data charts	Visualization
dataset.export_with_chart	Export data and charts	Export

Tool Categories

Metadata Tools

Used to get meta-information about semantic layer models and fields:

• get_metadata - Get overview of all available models
• describe_model_internal - Get detailed field definitions of a single model

Suitable for understanding data structure before querying.

Query Tools

Used to execute structured data queries:

• query_model - Supports complex filtering, sorting, grouping, aggregation

Requires understanding of semantic layer models and query syntax, provides precise query control.

Natural Language Tools

Used for intelligent data queries:

• dataset_nl.query - Describe query requirements in natural language

No technical knowledge needed, suitable for regular business users.

Visualization Tools

Used to generate charts:

• chart.generate - Generate trend charts, bar charts, pie charts based on data

Export Tools

Used to export data:

• export_with_chart - Export query results and charts

Role Permissions

Different endpoints provide different tool sets:

Permission Matrix

Tool	Admin	Analyst	Business
dataset.get_metadata	✅	✅	❌
dataset.describe_model_internal	✅	✅	❌
dataset.query_model	✅	✅	❌
chart.generate	✅	✅	❌
dataset.export_with_chart	✅	✅	❌
dataset_nl.query	✅	❌	✅

Endpoint Description

Endpoint	Role	Tool Scope
/mcp/admin/rpc	Admin	All tools
/mcp/analyst/rpc	Analyst	Metadata + Query + Visualization
/mcp/business/rpc	Business User	Natural language only

Typical Workflows

Analyst Workflow

1. get_metadata          → Get available model list
2. describe_model_internal → View model field details
3. query_model        → Execute data query
4. chart.generate        → Generate chart (optional)

Business User Workflow

1. dataset_nl.query      → Describe requirements in natural language, done in one step

Tool Call Examples

MCP Protocol Call

{
  "jsonrpc": "2.0",
  "id": "1",
  "method": "tools/call",
  "params": {
    "name": "dataset.get_metadata",
    "arguments": {}
  }
}

Response Format

{
  "jsonrpc": "2.0",
  "id": "1",
  "result": {
    "content": [
      {
        "type": "text",
        "text": "{...}"
      }
    ]
  }
}

Error Handling

Tool calls may return the following errors:

Error Code	Description
-32600	Invalid request
-32601	Method not found
-32602	Invalid params
-32603	Internal error

Error Response Example

{
  "jsonrpc": "2.0",
  "id": "1",
  "error": {
    "code": -32602,
    "message": "Missing required parameter: model"
  }
}

Performance Tips

1. Get metadata first: Use get_metadata before querying to understand available models
2. Use pagination: Use start and limit parameters for large data queries
3. Add filter conditions: Avoid full table scans, use slice to filter data
4. Select necessary fields: Only query needed columns to reduce data transfer

Next Steps

• Metadata Tools - Get model and field information
• Query Tools - Execute structured queries
• Natural Language Query - Intelligent data queries