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 query, timeWindow, and pivot	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 filtering, sorting, grouping, aggregation, calculated fields, timeWindow, and pivot

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
5. Use the right advanced mode: Use timeWindow for YoY/MoM/rolling analysis and pivot for cross-tabs; do not combine them in one request

Next Steps

• Metadata Tools - Get model and field information
• Query Tools - Execute structured queries
• Natural Language Query - Intelligent data queries
• Extensions - Data Viewer and optional extensions